A sharp essay has been moving through my feed this week. Milena Mardahay argues that nursing’s safe-staffing evidence, the Aiken body of work most of all, rests on a correlation the profession has learned to treat as proof, and that a case built on the weakest available inference hands opponents an easy refutation. She is right about more of it than the field will want to admit.

This is not an indictment of the researchers, who have built most of what we know about the nursing workforce. It is a claim about the ground the whole argument has been fought on. And I think both the researchers and their critics have made the same quiet assumption: that the resource in question is the number of nurses.

Almost the entire debate, thirty years of it, is organized around a ratio. Patients per nurse, and whether moving that number moves mortality. But the ratio was never the resource. It is a proxy for one, and a leaky proxy at that. It tells you how many patients a nurse is responsible for. It says almost nothing about how much care each of those patients receives.

The ratio was never the resource

The resource is care-team hours at the bedside, and two things drain them out of any fixed ratio.

The first is the support layer the ratio ignores entirely. On a typical unit, nursing assistants can carry one to two or more hours per patient day of delegable work. Take that away and the nurse absorbs it, spending hours on tasks that never needed a license or the assessment and judgment only a nurse can provide. And the harms we track most closely, falls, pressure injuries, CAUTI, are heavily influenced by the frequent preventive work that assistants are the ones delivering: turning, toileting, mobility, hygiene. A model that counts only licensed nurses is blind to the support layer that moves those numbers.

The second is that a staffed hour is not a care hour. As documentation and administrative burden climbs, and it has climbed for years, the same ratio delivers less and less actual time in the room. Two units with identical staffing on paper can deliver very different care, because the paper never accounted for how much of the nurse’s hour the system had already claimed. We can measure this. The audit logs already hold it. We mostly choose not to look.

And the demand on those hours has not held still either. A 2024 population-based study in JAMA Internal Medicine, following 3.4 million hospital admissions over fifteen years, found patients steadily growing more complex: admissions were about 2.7 times as likely to come through the emergency department, nearly twice as likely to involve ten or more active medications, and half again as likely to carry a heavy burden of chronic illness. A ratio is a fixed promise made about a moving target. Same number on paper, a sicker patient in the bed, and more hours required to reach the same standard of care.

Why the budget makes it structural

This is where it stops being just an operational issue. Under the current model, nursing is bundled into the room rate, a flat charge that never flexed as patients grew sicker. It is not paid as its own service. That means the nursing budget is close to fixed, and inside a fixed budget every choice is a trade. Raise the most expensive licensed hours, as a ratio mandate does, and the cheaper support hours are the slack that gives.

It is also why I read the California evidence differently than its headline results suggest. The mandate regulated the licensed nurse ratio and left the support layer untouched, inside a budget that could not grow. If support hours fell while RN numbers rose, then the finding that falls and pressure injuries did not improve is close to what you would predict, because the law forced up one layer and let the budget cut the layer that actually moves those outcomes. On that reading, California did not fail to prove that staffing matters. It showed what happens when you regulate the wrong hours inside a closed pool.

The layer beneath the bedside

And there is one more layer, deeper still. Even “care-team hours at the bedside” names the resource too narrowly, because safe, effective, and affordable care depends just as much on hours spent deliberately away from the bedside. Coverage so nurses can take the time off they have earned. Orientation for new hires. The ongoing education that keeps competency current as patients grow more complex. The administrative and environmental work that keeps a unit running. None of it is direct care, and all of it is what makes direct care possible.

These hours are the easiest thing in the building to write off as overhead, and inside a fixed budget they are the first to go. But a nurse who can never get time off burns out and leaves, or stays and makes the error we were trying to prevent. A unit that cannot orient cannot absorb its own turnover. Deferred education surfaces later as thinner judgment at the bedside, exactly as the average years of experience on the floor keep falling. Starve the non-patient-care time and you do not trim a luxury, you lower the ceiling on the care that can be delivered. I can show it mathematically: as the resources reserved for that time shrink, the capacity for patient care shrinks with them. They move together.

So I would put the argument this way. The correlation fight is a trap, and both sides are standing in it. You do not need a natural experiment to establish the real failure, because it is already visible in the arithmetic. The most common formula used to build nursing budgets understates the positions a unit actually needs, so the hours that decide whether a nurse can work at the top of their license are undercounted before the first shift is ever scheduled. You can show that on paper, before anyone dies. Framed correctly, understaffing is a problem of fatigue, burnout, moral injury, and lost productivity long before it is ever a problem of mortality, and every one of those costs lands on the system’s own ledger.

Nursing has spent three decades trying to prove a correlation when the more defensible claim underneath it is structural and economic. The number of nurses was never the thing. The hours are, and we do not pay for them as if they were care, because our payment model does not recognize nursing as care at all. That is the level the argument belongs on, and it is the one that survives exactly the scrutiny the critics are bringing.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and conceptual approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

On June 24 a federal court handed nursing a win. A judge granted a nationwide preliminary injunction blocking the part of the Department of Education’s RISE rule that left post-baccalaureate nursing off its list of professional degrees, the classification that would have cut graduate nursing students’ federal borrowing limits roughly in half. The court found the narrowed definition likely unlawful and the challengers likely to prevail. For now, future nurse practitioners, nurse anesthetists, and nurse-midwives keep access to the higher limits.

It is an interim ruling, not a final one, and the broader RISE changes take effect today, July 1. But the win marks something nursing rarely manages at this scale. Over the last few weeks, the profession mobilized.

The response to the rule was fast and organized. Twenty-four state attorneys general and the District of Columbia sued. The American Nurses Association and nine other nursing organizations filed their own suit. A coalition of professional associations, led by the nurse practitioners, brought the case that produced the June 24 injunction. Legislators moved, and a bill to fix the classification was introduced. The legal argument was straightforward: the agency had narrowed a definition Congress already wrote into law. The court agreed enough to act, staying the exclusion nationwide and ordering the Department to fall back to the broader, preexisting definition while the case proceeds.

That is real power, and the injunction proves it. Nursing does not assemble it often. Attorneys general, national associations, legislators, and public attention pointed at a single nursing economics issue at the same time. So where is that power aimed?

It is aimed at a symptom.

The loan-limit exclusion is not the disease. It is the latest expression of a deeper condition: nursing is not counted or funded as the value it produces. On the inpatient side, nursing care is not billed as its own service. It is folded into the room rate and absorbed into the bundled payment for the stay. A contribution that is never separately measured cannot be separately negotiated, advocated for, or recognized in the downstream models that policy decisions like RISE rely on. The classification that left nursing off the list was drawing on a system that was never built to see nursing in the first place. Briana Posanka and I traced that reimbursement architecture in detail in an earlier piece on how nursing’s value goes uncounted in the payment system.

If that sounds familiar, it should, because this is the pattern nursing keeps living.

We treat understaffing as a recruitment problem and run harder at the pipeline, while units stay short. We treat burnout as a personal deficit and offer resilience training, while the conditions that cause it hold. We treat incivility and turnover as culture problems and run more surveys. And now we treat a loan exclusion as a classification problem. Every one of these fights is winnable. Several are worth winning right now. But they keep coming back, because each one is a response to a symptom while the thing underneath stays exactly where it was.

Underneath all of it is how nursing is counted and funded. The most common formula taught in nursing finance education builds the staffing budget short before the fiscal year begins. The reimbursement structure renders nursing economically invisible. Those two facts generate the understaffing, the moral injury, the turnover, and yes, the workforce-pipeline pressure that makes a fight over education financing matter in the first place. Fix the symptom and you win a round. Fix the root and the symptoms stop recurring.

So why does the root never get the energy that RISE just got?

Because the root lacks what RISE had. RISE had a clear target, the agency. It had a deadline, July 1. It had a single visible decision to rally against. Those three things make mobilization easy. The root cause has none of them. Reimbursement invisibility and a flawed staffing formula are diffuse and technical. They unfold over decades rather than in a news cycle, and part of the problem is homegrown: the formula lives in nursing’s own textbooks and continuing education. There is no clean external enemy to sue. There is only a quiet structural fact that has been normalized for so long that most of the profession no longer sees it.

That is the real work, and it is not only redirecting energy. It is giving the root cause the urgency the symptom gets for free.

That is what the language of patient safety is for. A staffing budget that is short before the year starts is not an accounting curiosity. It is a system set up to deliver less care than it claims to fund, and the consequences are measured in falls, in failure to rescue, in moral injury, and in nurses leaving. When a deviation like that produces no immediate, visible catastrophe, organizations stop treating it as a problem and start treating it as the baseline. That is normalization of deviance, and it is exactly how a structural error survives in plain sight for decades. Naming it that way is how the abstract root starts to feel as urgent as the visible rule.

Here is the opportunity. The coalition that formed around RISE is the same coalition the root needs, and the argument is the same shape: an institution applying standards that do not hold up, failing to recognize nursing’s value. That argument does not stop at the Department of Education. It runs straight upstream into how nursing is reimbursed and how nurse staffing is budgeted.

So to everyone who showed up for this fight, on the Hill, in the courts, in the associations, in the comments: thank you, and keep going. You just won the injunction. The case is not over, so finish it. Then turn that same energy on the reimbursement structure and the staffing math underneath it, while the profession still has its attention pointed in one direction.

Nursing just proved it can move. The next move is upstream.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and conceptual approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

On April 30, 2026, the Department of Education finalized the Reimagining and Improving Student Education (RISE) rule and explicitly declined to classify post-baccalaureate nursing degrees as professional degrees for the purpose of federal student loan limits. Medicine and dentistry were included. Nursing was not.

The classification was not based on an earnings analysis. The Department designated eleven specific fields, including medicine, dentistry, law, and pharmacy, by four-digit CIP code. Nursing shares no four-digit CIP code with any of them, even though it sits in the same broad two-digit health-professions family, CIP 51, as medicine, dentistry, and pharmacy. Commenters asked the Department to classify at that two-digit level, which would have included nursing. It declined. The relevant question is not why the DOE underestimated nursing’s earnings. It is why nursing was never on that list in the first place.

Lists like this one reflect historical visibility: which professions were structured, at the point of categorization, to be economically distinct and administratively recognizable. Nursing’s absence is not an oversight. It is a downstream artifact of the same architectural condition that shapes nursing’s economic profile today.

To be precise about the Department’s reasoning, the exclusion did not rest on earnings or on any judgment of nursing’s worth. The rule applies a three-part test: a professional degree must mark the completion of academic requirements for beginning practice in the field, require skill beyond a bachelor’s degree, and generally require licensure. The Department read advanced nursing degrees as failing the first prong, reasoning that a person can enter nursing practice with an associate or bachelor’s degree, so a master’s or doctorate marks advancement rather than entry. That reading is contested. Nursing organizations argue the test should turn on entry into the specific advanced role, where a nurse anesthetist or nurse practitioner cannot begin practice without the graduate degree, and the question is now being litigated. We take no position on it.

Our point sits one level beneath that dispute. Whichever reading prevails, the question underneath remains the same: why does nursing have a bachelor’s entry point and a fragmented advanced tier while medicine has a single doctoral gate? That is the same architecture this piece is about. A profession whose contribution was never structured to be financially visible was also never structured into the single, economically distinct credential path these categories were built to recognize. The structure is the artifact.

The Reimbursement Architecture

Nursing is one of the largest licensed health professions whose clinical services are not separately reimbursable under core federal inpatient payment structures. Physician services generate discrete billing. Nursing services do not. They are bundled into the room rate as a cost and absorbed into the DRG payment the hospital receives for the stay, never billed as a service of their own. That constraint is not cosmetic. It determines what is financially visible, and visibility determines what is economically possible at every layer downstream.

DRG weights reflect case complexity, and some value-based payment models incorporate nurse-sensitive quality metrics. But case-mix weighting is not billing. Nursing’s contribution is absorbed into a rate the system calculates; it does not generate a discrete financial claim. A contribution that cannot be separately measured cannot be separately negotiated, advocated for, or made visible in downstream policy models.

Advanced practice nurses hold partial billing rights under Medicare Part B, at 85 percent of the physician rate, primarily in outpatient and independent practice settings. That exception is real but narrow. The majority of advanced nursing degree holders are hospital-employed, and in that context compensation is set as an institutional wage by finance teams, not derived from individual billing capacity. The salary profile that the broader nursing labor market produces is built overwhelmingly from this hospital-employed workforce, and that workforce’s compensation is shaped by what follows.

A reimbursement structure that makes nursing invisible as revenue produces a budget that treats nursing labor as pure cost. Budget-level underfunding flows into position control, where authorized headcount gets set too low to cover actual patient demand. Schedules get posted with gaps. Some gaps get filled with premium labor: agency nurses, travel nurses, at two to three times staff rates. Others go unfilled, leaving remaining staff to absorb higher patient loads and accelerating the burnout that drives permanent vacancies. Premium labor consumption compresses margins. Compressed margins put downward pressure on base wages for the permanent nursing workforce. Inadequate staffing degrades the patient outcomes nursing exists to protect.

The mechanism is not that nursing can only be paid from nursing-specific revenue. In cost-center accounting, nursing labor competes for margin against every other operational expense with no revenue offset to point to. When premium labor costs compress that margin, the adjustment lands on base wages. The COVID-era travel nurse spending surge documented this in real time: as agency costs reached unprecedented levels across hospital systems, permanent staff wage growth did not keep pace and benefit structures tightened.

The DOE made a legible administrative decision using a categorization system that has never included nursing. That system did not fail to see nursing’s value. It was built in a context where nursing’s value was never structured to be separately visible.

The Cycle This Produces

When nursing’s economic contribution is invisible, the consequences extend well beyond salary. A profession whose value cannot be directly measured in financial models becomes a profession whose value gets discounted in every downstream decision, from budget allocations to policy classifications.

This creates a self-reinforcing pattern. Invisible value produces constrained compensation. Constrained compensation produces the salary profile that appears in labor market data. That profile shapes the administrative categories that policy decisions like the RISE rule reflect. And those decisions further restrict who can access the advanced education the profession most needs filled.

The students most at risk of being shut out are first-generation students, lower-income students, working nurses without financial safety nets, and students from historically marginalized backgrounds. The roles shut out are nurse educators, nurse scientists, nurse leaders, and advanced practice nurses in underserved settings. These are not peripheral roles. They are the roles with the largest workforce gaps and the broadest long-term impact on the systems nursing sustains.

A nurse educator may earn less than a bedside clinician while simultaneously producing the future nursing workforce. A nurse scientist may earn below many clinical roles while generating the evidence base that improves care at scale. If borrowing policy is calibrated to compensation, and compensation is calibrated to a structurally distorted market, policy will keep underestimating the value of the roles society most needs to fill.

Income-driven repayment, public service loan forgiveness, and employer tuition benefits exist as partial offsets. Income-driven repayment programs face ongoing policy instability. Loan forgiveness requires a decade of qualifying employment. Employer tuition assistance is typically capped well below graduate program costs. For a working nurse making an enrollment decision today, none of these closes the gap the annual borrowing limit creates.

Policies designed to protect students from excessive debt may be restricting the very pipeline those students are needed to sustain.

What Would Actually Fix This

Not the classification.

Reclassifying nursing as a professional degree for loan purposes may be an appropriate short-term corrective, but it addresses a downstream symptom rather than the upstream architecture that produced it. The classification reflects a categorization system. That categorization system reflects, in part, which professions have been financially visible within federal payment structures. And that visibility is the part that needs to change.

The mechanism exists in adjacent contexts. Medicare Part B reimburses nurse practitioners for defined clinical services outside the inpatient bundle. Extending comparable visibility to inpatient nursing services, whether through defined billable encounters or nursing intensity adjustments to DRG weights, would begin to surface what the current structure obscures. That changes what compensation is structurally possible. That changes what labor market data reflects. The sequence runs in that direction, not the reverse.

This is not hypothetical. Nursing intensity weighting has a research lineage that runs for decades, including John Welton’s work on nurse staffing intensity and the resources individual patients consume, and it is the focus of active advocacy, including the American Nurses Foundation’s Reimagining Nursing Initiative.

Advocacy focused on the label without addressing the mechanism is advocacy that will need to be repeated. The system will keep producing the same outputs until the inputs are changed.

Why We Are Writing This

We do not share a political position on the RISE rule. We share a framework for reading it.

What we are describing is not an argument about whether nursing deserves recognition. It is a structural diagnosis of why nursing’s economic profile looks the way it does, and why that profile keeps generating consequences that nursing professionals keep being asked to absorb.

Reimbursement design is not the only force that shaped this structure. Accessible entry into nursing was a deliberate choice that served workforce supply, access, and the second-career and first-generation students the profession depends on, and a long history, much of it gendered and hospital-bound, runs alongside the payment architecture. We focus on reimbursement because it is the layer most directly downstream of policy and most open to change, not because it is the whole story.

Invisible value is not a perception problem. It is a design problem. And the design is upstream of every policy, every salary negotiation, every workforce report, and every administrative category that follows from it.

Until the architecture changes, the consequences will keep compounding.

Briana Posanka, MSN, RN, CNE and Robert Wingo, RN, BSN, NI-BC are FINE Fellows at the Commission for Nurse Reimbursement. Briana hosts Unbundled: Nursing, Value, and the Cost of Care, a microlearning podcast series examining how healthcare payment models and policy decisions shape nursing practice and patient outcomes.

The morning of January 28, 1986, engineers at Morton Thiokol tried to stop the Challenger launch. They had documented their concerns in writing months earlier. The O-rings sealing the solid rocket boosters were not designed to function in the overnight temperatures that had fallen across Cape Canaveral. They had evidence. They had charts. They said: do not launch.

NASA managers pushed back. Under pressure, Thiokol management reversed course and authorized the launch. Seventy-three seconds after liftoff, Challenger broke apart. Seven crew members died.

Sociologist Diane Vaughan spent years studying what happened. Her 1996 book, The Challenger Launch Decision, gave a name to the mechanism she found: normalization of deviance (Vaughan, 1996). When an organization encounters a rule deviation or warning signal that does not immediately produce a catastrophe, it begins to treat the deviation as acceptable. The warning stops being a warning. It becomes the standard. Over time, the deviance becomes normal, and the people inside the organization cannot see it anymore.

The O-rings had eroded on previous flights. Each time, the mission had succeeded anyway. Engineers raised concerns. Managers weighed the concerns against the launch schedule, the cost pressures, the political visibility of the shuttle program, and they authorized flight. The system worked, again and again, until it did not.

Challenger is often invoked as a communication failure, proof that the engineers simply presented their data badly. That lesson is too small. Better slides would not have undone the years in which O-ring erosion was observed, tolerated, and reclassified as an acceptable risk. The presentation problem, if there was one, sat downstream of a deviation the organization had already stopped seeing. The same holds in nursing finance. The formula error is not a message poorly explained. It is a deviation the profession normalized.

Normalization of deviance is not solely a NASA problem. Vaughan’s framework describes what happens inside any organization when warning signals are consistently managed away rather than investigated. The pattern requires only two conditions: a deviation that does not produce an immediate, visible catastrophe, and an institutional structure that creates pressure to proceed regardless.

Both conditions are fully present in nursing finance.

The Error

For sixty-six years, many nurse staffing budgets have been built on a formula that contains a mathematical error. The error produces a budget that is short by two to six percent before the fiscal year begins, before a single staffing decision is made, before a single shift is built.

William J. Ward, Jr., MBA, Associate Professor of Health Finance and Management at the Johns Hopkins Bloomberg School of Public Health, identified the error directly in his healthcare budgeting textbook:

“Even those skilled in budget calculations often make the mistake of saying, ‘Let’s add 15% for nonproductive time.’ But this approach is clearly incorrect and will always result in a budget that lacks sufficient staff.” (Ward, 2016, pp. 129-130)

The proof is short.

Start with a unit that needs 80 productive FTEs to deliver direct patient care. Nonproductive time, which includes paid time off, holidays, sick time, and required education, represents 20% of total paid time. Twenty percent is higher than current US rates and lower than some international benchmarks. It is used here because it produces round numbers that make the arithmetic transparent and the error easy to identify. The remaining 80% is available for care.

The numbers are given: 80 productive FTEs, 20 relief FTEs, 100 total FTEs.

80 + 20 = 100.

The question any nurse leader faces when building a staffing budget is: given 80 productive FTEs required, how many total FTEs must be budgeted?

The standard approach in use across the nursing literature and CE offerings applies the nonproductive rate to the productive FTE count to determine the relief staff to add:

80 × 0.20 = 16 relief FTEs

80 + 16 = 96 total FTEs

The budget is short by four FTEs. We started with 100. The formula produces 96.

Where did the 4 FTEs go?

The formula treats the nonproductive rate as a percentage of the productive workforce. The rate is a percentage of the total workforce. The 16 relief FTEs are subject to the same 20% nonproductive rate as every other position in the budget. To find the correct total number of relief FTEs, apply the same division to the 16:

16 ÷ 0.80 = 20 relief FTEs

20 − 16 = 4 FTEs recovered

The four recovered FTEs cover the nonproductive time of the relief staff themselves. The correct total:

80 + 20 = 100 total FTEs

Or, applying the division directly to the productive FTE requirement:

80 ÷ 0.80 = 100 total FTEs

The budget is whole.

Ward puts it plainly: “Avoid the mistake of merely adding 15% of nonproductive time back to the 17 workers. As demonstrated in Table 6.14, doing so will short change the FTE count and leave a manager short staffed even before the fiscal year begins.” (Ward, 2016, p. 129)

Sixty-Six Years of Evidence

The error did not start with any individual. It was not introduced by one careless author or one bad textbook. Other valid calculation approaches for nursing workforce planning exist; this analysis addresses the one that appears most consistently across the nursing literature, professional education, and practice settings. Tracing it requires following a line that runs through six decades, multiple countries, and the professional organizations responsible for nursing education itself.

1960 | United States

Sister Mary Laura Gunn’s 1960 monograph, A Method for Developing a Master Staffing Plan for the Nursing Service Department, published by the Catholic Hospital Association, contains the earliest known instance of the calculation error (Gunn, 1960). Gunn was a graduate of a hospital administration program at Saint Louis University. Her diagnosis of the problem was correct: nursing shortages were in many instances “the result of faulty planning by the administrators of the nursing service department.” Her formula was not. At the 6% nonproductive rate she used, the structural shortfall was approximately 0.36 FTEs per 100 budgeted staff. That amount disappears inside normal scheduling variance. At 6%, the formula appeared correct because it nearly was.

1979 + 2003 | United Kingdom

Keith Hurst’s systematic review of nursing workforce planning methods, commissioned by the UK Department of Health and published in 2003, documented and credited W.A. Telford as the originator of the additive formula in UK nursing, attributing it to a 1979 article in Hospital Health Services Review and a 1983 government report for North Birmingham Health Authority (Hurst, 2003). Hurst named Method 1 the Telford Method, but the additive formula error runs through all five workforce planning approaches in the review. Each method uses a different process to estimate care hours required; all five then feed that estimate into the same flawed conversion step. Where he shows the arithmetic explicitly, structural shortfalls reach approximately 5% at the 22% time-out rate then in use. The Department of Health adopted the review as NHS guidance. What Hurst transmitted as Telford’s method, the Department of Health adopted as policy.

1984 | United States: The Warning That Did Not Travel

Two years before the additive formula appeared in a major US nursing journal, the primary graduate textbook in nursing budgeting placed the correct formula on record and labeled the alternative wrong.

Budgeting Concepts for Nurse Managers, edited by Steven Finkler and published by W.B. Saunders, presented both approaches in Chapter 4. A footnote on page 73 stated: “Although the method described here is widely used, it is not theoretically correct” (Graf, 1984, p. 73). The footnote named the mechanism: staff hired to cover nonproductive time are themselves nonproductive some fraction of the time. It presented the division formula as the correct alternative and noted that the difference “can amount to a large amount of money when accumulated for all staffing needs for all nursing units.” The 1992 second edition carried the same acknowledgment. The correct formula was in the primary graduate text from 1984 forward. It did not stop what followed.

1987 | United States

Leah Strasen’s Key Business Skills for Nurse Managers, published by J.B. Lippincott in its Nursing Management Series, presented the additive formula without qualification (Strasen, 1987). The worked example at pages 136-137: 28.8 patient care FTEs × 1.15 (benefit time = 15% per FTE) = 33.1 total FTEs. The correct total at 15% nonproductive is 33.88. Strasen was a vice president of nursing at Henry Mayo Newhall Memorial Hospital in California, writing in a series for nursing leader professional development.

1999 | United States: Published by the Editor

The June 1999 issue of Nursing Management published “How to Develop a Unit Personnel Budget” (Brown, 1999). The formula: (ADC × NHPPD × 1.4 × 1.14) / 7.5 = total caregiver FTEs. The 1.14 multiplier applies a 14% nonproductive rate to the productive FTE count. The correct total at 14% nonproductive requires dividing by 0.86. The structural shortfall is approximately 2%.

The author, Barbara Brown, was the Clinical Editor of Nursing Management at the time of publication. The journal’s own editor published the error in the journal she edited. The formula Brown introduced, with the same 1.14 multiplier and additive structure, appears in a graduate nursing administration textbook published twenty-one years later.

2006 | International: The Error That Traveled

The International Council of Nurses published Safe Staffing Saves Lives as an International Nurses Day toolkit in 2006, designed for use across its member countries worldwide (International Council of Nurses, 2006). Annex 6, explicitly adapted from Hurst’s 2003 UK Department of Health review, reproduced the additive formula. The worked example: 455 hours × 1.22 (time-out factor) = 555.1 hours ÷ 37.5 hours per WTE = 14.8 WTEs required. The correct calculation: 455 ÷ 0.78 = 583.3 hours ÷ 37.5 = 15.56 WTEs. The structural shortfall at 22% nonproductive is approximately 4.9%.

The error traveled from a UK government-commissioned systematic review to a Geneva-based federation of national nurses’ associations with member organizations across the globe. The 22% nonproductive allowance it presented as an international benchmark became the figure a US book on nurse staffing carried across three successive editions as the basis for its 1.22 multiplier.

2018 | United States: Continuing Education

The May 2018 issue of Nursing Management published “Developing a Staffing Plan to Meet Inpatient Unit Needs,” carrying CE credit toward ANCC and AACN certification renewal (Hunt, 2018). The instructional method was the additive formula. Nurse leaders applying this formula would budget 2 to 4 fewer FTEs per 100 than their units required, depending on the nonproductive rate in use. By 2018, Ward’s correction had been in print for two years. The footnote in Graf and Finkler identifying the correct formula had been on record for thirty-four years.

2020 | United States

Management and Leadership for Nurse Administrators, eighth edition, published by Jones & Bartlett Learning, presents the staffing budget formula in Box 8-3 of Chapter 8:

(ADC × HPPD × 1.4 × 1.14) / 7.5

The formula is structurally identical to the one Brown published in Nursing Management in 1999: same 1.14 multiplier, same additive structure. The chapter author, Patricia Thomas, is also one of the book’s three editors. Sixty years after Gunn, the error appears unchanged in a graduate nursing administration text used to train the people who will teach the next generation of nurse leaders (Thomas & Roussel, 2020).

2026 | United States

By April 2026, the formula was still being taught to nurse leaders as continuing education. An ANCC-accredited CE workshop delivered to practicing nurse leaders this spring showed the following at Step 5 of its staffing plan build:

2,080 Annual hours for 1 FTE
281.7 Hours of non-productive time

281.7 ÷ 2,080 = 13.5% lost time
47.86 FTE + 13.5% = 54.4 + 1.0 = 55.4 FTE needed

The 1.0 is a nurse manager FTE added separately. The core calculation, 47.86 FTE + 13.5%, is the additive formula, identical in structure to the one Gunn used in 1960. The error has not been corrected. It is being actively taught. That workshop was not isolated. Four additional nursing leadership workshops attended in the preceding five years each presented the same formula without correction.

The individuals and organizations documented here are not the problem. Each reproduced a methodology the profession had treated as authoritative for decades before they encountered it. The error was not visible at the nonproductive rates when the earliest sources were written. By the time rates had grown large enough to produce operationally meaningful shortfalls, the formula had accumulated the authority of textbooks, government guidance, international standards, and CE certification. The formula is the problem. The people who transmitted it were working within the professional infrastructure as the profession had defined it.

Why the Error Survived Correction

The question is not only why the error appeared. It is why, after sixty-six years and two published corrections, it is still being taught.

At 6%, the error is nearly invisible.

Gunn’s 1960 formula used a 6% absence allowance, and, at that rate, the additive formula produces a shortfall of roughly 0.36 FTEs per 100 budgeted staff. A hospital using a 6% nonproductive rate and the wrong formula would budget 99.6 total FTEs where the correct answer is 100. The gap disappears inside normal scheduling variance. No audit catches it. No manager notices it. The formula appears correct because at low rates, it nearly is.

Leave entitlements did not stay at 6%.

Over the following decades, vacation allowances increased. Sick leave expanded. Mandatory continuing education became a credentialing requirement. Orientation periods grew as patient care complexity increased. Each addition increased the percentage of time unavailable for direct care. By the time the 2000s sources were published, nonproductive rates of 13 to 24 percent were common across the global nursing finance literature. At 20%, the additive formula produces a shortfall of 4.0 FTEs per 100. At 24%, the shortfall is 6.1 FTEs per 100.

The formula did not change. The shortfall grew.

This is the mechanism that kept the error hidden through its most consequential decades. At the rate it was introduced, it was undetectable. By the time the nonproductive percentage had grown large enough to produce operationally meaningful understaffing, the formula had accumulated decades of institutional authority. Questioning it required questioning textbooks, credentialing programs, government frameworks, and the accumulated professional knowledge of every finance director who had trained nurse leaders on how to build a budget.

Two additional features of nursing finance prevented detection.

Budget variance analysis does not examine the formula. It measures actual spending against the budgeted FTE count. A unit consistently running short-staffed generates a productivity variance, a turnover flag, or a premium labor overage. None of those analyses look upstream at whether the budgeted FTE count was correctly derived. The error was invisible to the oversight mechanism because the oversight mechanism assumed the budget was correct.

No verification standard exists either. Any FTE calculation can be checked in one step: multiply the proposed total FTEs by the productive percentage and confirm the result equals the original productive FTE requirement. If the numbers do not match, the budget does not fund the care hours it claims to fund. That check does not appear in any of the more than forty published sources where the error has been documented, spanning six decades and multiple countries. It does not appear in the CE workshops where the error is taught. Without a verification step, no practitioner applying the formula has a built-in mechanism to detect the shortfall.

The Human Toll

Seven people died when Challenger broke apart on January 28, 1986. Their names are Christa McAuliffe, Gregory Jarvis, Judith Resnik, Francis Scobee, Ronald McNair, Ellison Onizuka, and Michael Smith. A presidential commission was convened within days. The cause was identified within months. The program was grounded. Procedures were changed.

No equivalent investigation has been launched for nursing finance.

Three decades of peer-reviewed research has established a direct relationship between nurse understaffing and patient mortality.

Aiken and colleagues, studying 168 Pennsylvania hospitals, found that each additional patient added to a nurse’s assignment was associated with a 7% increase in 30-day mortality and a 7% increase in failure to rescue (Aiken et al., 2002). Needleman and colleagues, studying a Magnet-designated hospital where actual mortality ran 39% below predicted rates, found that each shift in which registered nurse staffing fell 8 or more hours below target was associated with a 2% increase in patient mortality; 34.6% of patients were exposed to three or more such shifts during their admission (Needleman et al., 2011). A 2026 study in JAMA Network Open found that nurse understaffing during the prior 24-hour period was associated with increased odds of in-hospital death, adjusted odds ratio 1.22 (95% CI 1.11–1.34) (Morioka et al., 2026). The Joint Commission identified low nursing staffing as a contributing factor in 24% of 1,609 hospital-reported patient deaths and serious injuries reviewed as of March 2002 (Joint Commission, 2002).

The formula error does not only harm patients. It harms nurses.

Nurses working in hospitals with higher patient loads had significantly elevated odds of burnout and job dissatisfaction (Aiken et al., 2002). In a 2023 survey of more than 7,000 nurses conducted by the American Nurses Foundation, 56 percent reported burnout and 64 percent reported a great deal of job-related stress (American Nurses Foundation, 2023). Chronic understaffing creates conditions in which nurses are systematically unable to meet their professional obligations to patients. Rabin and colleagues identified chronic understaffing as a systemic cause of moral injury in healthcare workers, defined as the persisting distress following exposure to events that transgress deeply held moral beliefs (Rabin et al., 2023). A systematic review of nurses across eight studies found significant positive associations between moral injury and anxiety and depression, and a significant negative association with quality of life (Anastasi et al., 2025). Female nurses die by suicide at nearly twice the rate of the general female population: 17.1 per 100,000 compared to 8.6, with a relative risk of 1.99 (Davis et al., 2021).

None of these outcomes has been traced to the budget methodology, because the methodology has not been questioned. That is the mechanism Vaughan described: the deviation disappears into the background of ordinary practice, and its consequences are attributed to everything except the deviation itself.

At a 20% nonproductive rate, the shortfall is 4 FTEs per 100. At 22%, it is nearly 5. Applied to approximately 1.9 million hospital RN FTEs in the United States (Bureau of Labor Statistics, 2025), the structural gap represents millions of unfilled shifts annually, before a single call-out, turnover event, or census surge is added. Those shifts do not simply go unfilled. They are absorbed through overtime, premium labor, or the suppression of the paid time off and required education the nonproductive rate exists to fund — costs the budget did not account for, because the formula that should have funded them was short from the start.

The formula error is the structural floor beneath every other staffing metric. In 2025, the national RN vacancy rate stood at 8.6%, with the average hospital carrying forty-three unfilled RN positions against its budgeted FTE count (NSI Nursing Solutions, 2026). One in three hospitals reported a vacancy rate of ten percent or higher. On top of that vacancy gap, RN turnover ran at 17.6% for the year. Each departure left the position empty for an average of seventy-eight days before a replacement was hired. New hires enter orientation before reaching independent practice, a period during which preceptor coverage is divided between training and patient care. More than one in five newly hired RNs left within their first year, resetting the cycle before the orientation investment was recovered. These layers of depletion compound on a budget that understated the requirement before any position opened, before any nurse resigned, and before any new hire set foot on the unit.

Units operating at or below their structural baseline for patient care carry no buffer. Every approved vacation day, sick call, and mandatory education requirement, the exact categories the nonproductive rate exists to fund, becomes an unfilled shift that must be absorbed by the remaining staff, covered with premium labor, or left open. The nonproductive rate exists to fund this relief coverage in advance. The correct formula does that. The additive formula does not.

The question is not whether understaffing causes preventable deaths. That is established. The question is how much of the chronic understaffing documented across the nursing literature for decades has been structurally guaranteed by a formula no one corrected.

Seven people died when Challenger broke apart. We do not know how many patients died in units whose budgets were calculated with the wrong formula. That is not because the number is small. It is because the harm has never been concentrated in a single visible event, and events that are not visible are not investigated.

The Challenger parallels are not rhetorical. They are structural.

Vaughan documented that NASA managers had received written warnings from engineers before the launch. The information was available. It was not acted on. The institutional pressure to proceed, to stay on schedule, to avoid disrupting a program with significant political and financial visibility, created conditions in which the engineers who had the math right were overridden by the managers who had the authority.

In nursing finance, the same dynamic has played out across the literature and in professional education settings. The corrected formula, when raised, has been declined not on mathematical grounds but on institutional ones. One workshop instructor told participants that finance would not accept the correct formula, that using it would make them “think you’re trying to cook the numbers,” and that the correct approach simply “is not gonna fly.”

When the same error was raised in a separate CE context, the response was: “I’ve always done it this way and I’ve worked with 3 large consulting firms and they do it this way too.” That is the verbal signature of normalized deviance. The duration of a practice and the number of institutions that share it offered as evidence of its correctness. Vaughan’s framework predicts that response exactly.

The error has also evaded identification in peer review. The additive formula has appeared in refereed nursing journals without challenge, reviewed by editors and peer reviewers trained in the same tradition. When the people responsible for catching errors share the error, peer review cannot function as a corrective mechanism. The formula passes review not because reviewers evaluated it and found it correct, but because they recognized it and assumed it was.

The engineers had the math right. The authority was elsewhere.

The mechanism Vaughan described requires no malice. The Thiokol managers who authorized the launch were not indifferent to the crew. The nurse educators who continue to teach the additive formula are not indifferent to nurses or to patients. The deviation normalizes gradually, through repetition, through institutional endorsement, through the absence of a visible and attributable catastrophe. The formula is used. Budgets are filed. Units appear staffed to finance. The nurse manager knows the unit is short. The charge nurse knows. The patients know. Nurses leave. Patients wait. The shortage is attributed to supply.

The nursing workforce shortage has been debated in terms of both supply and demand for more than eighty years. Reports of an emerging shortage appeared as early as the mid-1930s, driven by rising hospital utilization, more technologically complex patient care, and reductions in nurse working hours (Barbara Bates Center for the Study of the History of Nursing, 2011). Supply responses dominated: expand nursing school admissions, shorten training pipelines, recruit internationally. Demand-side pressure has been recognized too. The complexity of hospitalized patients has grown continuously, hospital admission rates rose substantially across the postwar decades, and the proportion of direct care requiring a registered nurse rather than less trained personnel increased. Nursing positions sit open for weeks and months. Recruitment campaigns, immigration programs, accelerated training pipelines, international hiring, technology solutions: every major structural response has addressed one side or the other. But the most widely used formula for converting care requirements into funded positions has systematically understated the requirement. When facilities correct the calculation, many will find they need dozens more staff than their current vacancy reports reflect. The supply problem is real. The demand has never been accurately counted.

The implications extend beyond staffing budgets. How nursing labor is counted affects how it is reimbursed. That thread belongs in a separate analysis.

The profession that publishes and teaches this formula is the profession that can correct it. That is not a criticism. It is a description of both the problem and the opportunity. No external authority is required. Nursing already applies independent verification to medication dosing calculations because the profession has long recognized that arithmetic errors in clinical practice cause direct, traceable patient harm. A nurse who approximated a weight-based drug calculation rather than performing it correctly would face immediate review. The staffing formula has carried the same class of arithmetic error for sixty-six years without correction. The correction requires only the profession’s willingness to apply to its financial calculations the same standard of mathematical rigor it has always demanded at the bedside.

The check that would catch it takes thirty seconds: multiply the total budgeted FTEs by the productive percentage. If the result equals the productive FTE requirement, the budget is whole. If it does not, it is short before the fiscal year begins.

Every nurse manager, nursing director, and CNO can apply that check to their staffing budgets today.

Challenger is remembered not because the disaster was unforeseeable. It is remembered because it was foreseeable, and it happened anyway. The catastrophe in nursing is also visible, to anyone willing to connect it to its source.

References

Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA, 288(16), 1987–1993.

American Nurses Foundation. (2023). Pulse on the nation’s nurses: Mental health and wellness survey (May 2023). American Nurses Association. https://www.nursingworld.org/practice-policy/PulseontheNationsNursesSurvey/nurses-survey-may-2023/

Anastasi, G., Gravante, F., Barbato, P., Bambi, S., Stievano, A., & Latina, R. (2025). Moral injury and mental health outcomes in nurses: A systematic review. Nursing Ethics, 32(3), 698–723. https://doi.org/10.1177/09697330241281376

Barbara Bates Center for the Study of the History of Nursing. (2011). Where did all the nurses go? University of Pennsylvania School of Nursing. https://www.nursing.upenn.edu/nhhc/workforce-issues/where-did-all-the-nurses-go/

Brown, B. (1999, June). How to develop a unit personnel budget. Nursing Management, 30(6), 34–35.

Bureau of Labor Statistics, U.S. Department of Labor. (2025). Occupational employment and wage statistics profiles: 29-1141 Registered nurses (May 2025). U.S. Department of Labor. https://data.bls.gov/oesprofile/?major_group=290000&occupation=291141&measure=01&areas=INDUSTRY,STATE,MSA

Davis, M. A., Cher, B. A. Y., Friese, C. R., & Bynum, J. P. W. (2021). Association of US nurse and physician occupation with risk of suicide. JAMA Psychiatry, 78(6). https://doi.org/10.1001/jamapsychiatry.2021.0154

Graf, C. M. (1984). The operating budget. In S. A. Finkler (Ed.), Budgeting concepts for nurse managers (1st ed., pp. 62–103). W.B. Saunders.

Gunn, M. L. (1960). A method for developing a master staffing plan for the nursing service department. Catholic Hospital Association.

Hunt, P. S. (2018, May). Developing a staffing plan to meet inpatient unit needs. Nursing Management, 49(5), 25–31. https://doi.org/10.1097/01.NUMA.0000532326.62369.9b

Hurst, K. (2003). Selecting and applying methods for estimating the size and mix of nursing teams: A systematic review of the literature commissioned by the Department of Health. Nuffield Institute for Health, Leeds University.

International Council of Nurses. (2006). Safe staffing saves lives [International Nurses Day toolkit]. International Council of Nurses.

Joint Commission on Accreditation of Healthcare Organizations. (2002). Health care at the crossroads: Strategies for addressing the evolving nursing crisis. JCAHO.

Morioka, N., Moriwaki, M., Miyawaki, A., Saville, C., Fushimi, K., & Griffiths, P. (2026). Hospital nurse understaffing and patient mortality, readmission, and length of stay. JAMA Network Open, 9(2), e2558235. https://doi.org/10.1001/jamanetworkopen.2025.58235

Needleman, J., Buerhaus, P., Pankratz, V. S., Leibson, C. L., Stevens, S. R., & Harris, M. (2011). Nurse staffing and inpatient hospital mortality. New England Journal of Medicine, 364(11), 1037–1045. https://doi.org/10.1056/NEJMsa1001025

NSI Nursing Solutions. (2026). 2026 NSI national health care retention & RN staffing report. NSI Nursing Solutions, Inc.

Rabin, S., Kika, N., Lamb, D., Murphy, D., Stevelink, S. A. M., Williamson, V., Wessely, S., & Greenberg, N. (2023). Moral injuries in healthcare workers: What causes them and what to do about them? Journal of Healthcare Leadership, 15, 153–160. https://doi.org/10.2147/JHL.S396659

Strasen, L. (1987). Key business skills for nurse managers. J.B. Lippincott Company.

Thomas, P. L., & Roussel, L. (2020). Procuring and sustaining resources: The budgeting process. In L. A. Roussel, J. L. Harris, & P. L. Thomas (Eds.), Management and leadership for nurse administrators (8th ed., pp. 155–188). Jones & Bartlett Learning.

Vaughan, D. (1996). The Challenger launch decision: Risky technology, culture, and deviance at NASA. University of Chicago Press.

Ward, W. J., Jr. (2016). Health care budgeting and financial management (2nd ed.). Praeger.

Today is International Nurses Day. Florence Nightingale's birthday.

Most people know her as the lady with the lamp. She was also one of the most consequential data scientists in history.

In the Crimean War, she did not just care for soldiers. She gathered data, built visualizations, and proved that preventable disease was killing more men than battle wounds. Then she took that evidence to Parliament and forced change. Thousands of lives were saved not by more nursing, but by a nurse who understood the numbers and refused to be ignored.

I am a board-certified informatics nurse. That lineage is mine. It is yours too.

Last week I recorded a podcast with Teresa Sanderson of Dare to Do Nursing Differently. It was the first time I spoke publicly about my moral injury.

The source of it is a tally I have kept for nine years. Every nurse's story about a bad shift. Every burnout report, every turnover study, every resilience initiative aimed at nurses rather than the conditions nurses work in. Every flawed CE offering that teaches the wrong formula. Every textbook and journal article that carries it forward. Caregivers. Patients. Their families. The timeline spans decades. That much suffering, for a simple math error.

Because here is what a staffing budget built on the wrong formula does: it shorts the unit before the schedule is ever written, holds nurses accountable for conditions that were never adequately resourced, and leaves no visible decision to appeal. The system is functioning as designed. Just on the wrong math.

Nursing has been the victim of a 66-year-old middle school math error.

For every 100 nurses a unit actually needs, the formula is already shorting the budget by 2 to 6 positions. Before PTO. Before FMLA. Before call-outs. Before anyone says we just need to be more resilient.

Florence Nightingale did not accept the conditions she inherited. She quantified them, named them, and demanded correction with data.

We have that same opportunity today.

If you have ever felt like staffing was broken before the schedule was even written, watch this episode.

Podcast Link: The 66-Year-Old Math Error Quietly Causing Nurse Understaffing

Podcast Description:

Nurse managers have been blamed for staffing problems they may never have had the math to solve.

In this episode of Dare to Do Nursing Differently, Teresa Sanderson sits down with Robert Wingo, BSN, RN, NI-BC, a board-certified informatics nurse, to talk about a 66-year-old math error hiding inside nurse staffing budget formulas.

The impact?

For every 100 nurses a unit actually needs, the formula may already be shorting the budget by 2–6 positions.

And that is before PTO.
Before FMLA.
Before vacancies.
Before call-outs.
Before turnover.
Before anyone says, “We just need to be more resilient.”

This conversation matters because chronic understaffing is not just an operations issue.

It affects burnout.
It affects moral injury.
It affects incivility.
It affects patient care.
It affects whether nurse leaders are set up to succeed or quietly handed an impossible assignment.

Robert also challenges the language nursing has accepted for years: “productive” and “non-productive” time.

Because education, orientation, PTO, professional development, and recovery are not waste.

They are patient care support.

And when we cut patient care support, we are not becoming more efficient.

We are defunding the fuel and maintenance that keep nursing running.

Nursing has to understand the math.

Not because nurses need one more responsibility.

Because the people making financial decisions need to hear from nurses who can speak the language, challenge the assumptions, and name the real root cause.

Watch this episode if you have ever felt like staffing was broken before the schedule was even written.

On April 30, Becker’s Healthcare published a four-point breakdown of the staffing formula error I documented in April. KFF Health News covered it in their newsletter.

I will not recap the full piece here. The link is below. The short version: hospitals have been using the wrong formula to budget nursing staff for at least 66 years. The correct method divides. The common method multiplies. The compounding shortfall is large enough to affect patient outcomes and small enough to have gone undetected for decades.

Decades-old formula error may be causing hospitals to underbudget nursing staff: 4 notes [Becker’s Hospital Review]

Two articles are in progress.

The first covers a country that has this error embedded in its national workforce planning framework, and documents how the error has extended beyond nursing into another clinical profession entirely.

The second traces how an international workforce planning framework carried the error into one of the largest health systems in the United States, and how it is embedded there today.

Subscribe if you want to follow that thread.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and conceptual approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

What the Nurse Reattraction Research Gets Right, and What It Misses

New research from Lasater et al., with commentary from Squires and Jones in JAMA Network Open, surveys more than 4,000 registered nurses who left hospital employment to ask what would bring them back. Roughly two-thirds say they would consider returning. The top three conditions: adequate staffing (65%), flexible scheduling (59%), and better wages and benefits (59%).

The commentary pushes back on simplistic readings of that data and correctly identifies scheduling flexibility as an overlooked lever. But it stops at the organizational level. The more useful question is where those conditions come from.

The three complaints share one origin

Adequate staffing, flexible scheduling, and competitive wages are not three separate problems. They are one problem expressed three ways.

Hospital nursing care is bundled into the room rate. Nursing is the only clinical profession in a hospital that cannot directly bill for services rendered. There is no discrete nursing revenue line. There is no additional reimbursement for higher-acuity patients within a unit. As patient acuity rises across hospital populations, nursing workload per patient increases, but neither the revenue nor the budget adjusts to reflect it. The gap widens without any change to the financial structure. Nursing absorbs that constraint as the largest and most financially invisible cost center. Fewer positions, flat wages, and rigid scheduling are the downstream result.

The budget math makes it worse

The method most commonly taught and applied multiplies Patient Care FTEs by a coverage factor to account for Patient Care Support time, the funded coverage for leave, education, and orientation. That is wrong. The correct formula divides: Total FTEs = Patient Care FTEs ÷ Patient Care %.

The multiply method adds Patient Care FTEs without adding the Patient Care Support FTEs those nurses require for time off, education, and administrative time. Every nurse added to the bedside widens the gap. This 66-year-old error alone produces a 2-6% shortfall. When combined with other identified errors in the budget process, the total deficit ranges from 2 to 10 percent before vacancy rates are introduced. Vacancy rates compound it further.

The information structure

Decisions about nursing resources are built upward through a connected set of nurse staffing information structures, with reimbursement as the foundation. The budget error does not stay in the budget. Position control is underfunded from the start, funding fewer total FTEs than are required to deliver patient care and cover the time off, education, and administrative obligations that sustain it. Scheduling is built on that short base. Education and time off are deferred, not as independent management decisions, but as a direct consequence of the error already made upstream. Staffing fills what remains with overtime, agency labor, unfilled shifts, or staff floated from better-resourced units. Each path is costly: overtime and agency labor compress the margin further, putting downward pressure on the wage growth nurses identified as a condition for return; unfilled shifts drive burnout; floating pulls staff from units that managed their staffing better and is a documented dissatisfier, feeding the turnover the reattraction research set out to address. The cycle tightens with each pass.

Reattraction without structural repair is a revolving door

Nurses will return if conditions improve. But the reimbursement constraints and budget errors that produced those conditions remain. Conditions will not durably improve until the underlying structures change.

The more useful question is what financial structures prevent hospitals from funding the nursing workforce at scale. Answering it requires examining how nursing care is reimbursed, how that translates into budgets, whether the math used to build those budgets is correct, and whether the finance and staffing information pictures actually match. Until that work is done, nurses who return face the same conditions that drove the last cohort out. That is not reattraction. It is the same cycle restarted.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and logical approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

In February 2026, JAMA Network Open published a cohort study of 77,289 hospital admissions across 82 wards in Japan. The finding was direct: patients cared for by understaffed nursing teams face 22% higher odds of dying in hospital, higher readmission rates, and stays that are nearly a full day longer than those of adequately staffed patients. The study used propensity score matching and multilevel regression to control for confounding, and results were consistent across both methods.

The study defined understaffing not as falling below an arbitrary ratio, but as falling below each ward’s own typical staffing level. That distinction matters. Understaffing in this sense is not a failure to meet an external standard. It is a deviation from what the ward itself normally provides. When budget calculations systematically produce fewer FTEs than adequate staffing requires, that deviation is not occasional. It is structural.

A note on setting: the study was conducted in Japan, where nurse staffing levels are comparatively low by international standards. The authors address this directly, noting that their findings offer particularly relevant evidence for settings with comparatively low staffing standards, precisely because the associations hold at Japan’s lower baseline. The relationship holds across settings: evidence from England, the United States, Finland, and Switzerland cited in the same paper consistently shows that more nurses produce better outcomes.

The Attempted Fix

In March 2024, the Western Australia Government mandated nurse-to-patient ratios across public hospitals under policy MP 0187/24. The intent is right. Research consistently shows that patient outcomes deteriorate when nurse-to-patient ratios exceed safe thresholds. The legislature took that evidence seriously.

In February 2026, the Department of Health published the Nursing and Midwifery Workforce Planning Framework, Version 2.0, to support implementation. This document provides the step-by-step methodology facilities use to calculate the nursing workforce required to meet the mandated ratios: how many FTEs to budget, how to account for leave entitlements, how to convert patient care hours into a staffing plan.

It contains a structural calculation error documented in nursing workforce planning literature since at least 1960.

What the Framework Says

The framework calculates the total nursing workforce in two steps. First, determine the Patient Care FTEs required to meet patient demand at the mandated ratio. Then add non-productive FTEs to cover the workforce’s leave entitlements.

WA’s leave entitlement rates, as specified in the framework:

The framework applies each rate separately to the Patient Care FTE base, sums the results, and adds the total. To see the gap, start with the correctly staffed answer: a workforce of 100 total nursing FTEs at WA’s combined 23.46% non-productive rate contains 76.54 Patient Care FTEs and 23.46 Patient Care Support FTEs.

Now apply the framework’s method to those 76.54 Patient Care FTEs:

The methodology is systematic and documented. It appears rigorous. And it produces 5.5 fewer FTEs than a correctly staffed workforce of 100. A facility that should have 500 total nursing FTEs budgets only 472 under the framework. 28 FTEs missing before the year begins.

What the Calculation Is Actually Missing

The method asks: how much leave time do 76.54 Patient Care FTEs require? It calculates that answer, 17.96 FTEs, and adds it to the base.

The question it does not ask: who covers the leave time of those 17.96 non-productive FTEs?

Those FTEs take annual leave. They accumulate TOIL. They have accrued days off. They need professional development. They are absent from the workforce for the same reasons as every other FTE in the facility. The framework calculates their existence but never calculates their own leave requirements.

William J. Ward, Jr., Associate Professor of Health Finance and Management at the Johns Hopkins Bloomberg School of Public Health, described the same structural error in his 2016 textbook:

“Even those skilled in budget calculations often make the mistake of saying, ‘Let’s add 15% for nonproductive time.’ But this approach is clearly incorrect and will always result in a budget that lacks sufficient staff.”

The correct question is not “how much leave do my Patient Care FTEs need?” It is “what total workforce do I need so that the required number of FTEs is always available for patient care?” That question is answered by division.

Total FTEs = Patient Care FTEs ÷ Patient Care %

76.54 ÷ 0.7654 = 100.00. Not 94.50.

How the Error Got Into the Law

The WA framework did not originate in WA. It was adapted from the Queensland Health Business Planning Framework, 6th edition, published August 2021, itself originally published in 2001 and in continuous use since. Queensland’s framework is industrially mandated through Clause 39 of the Nurses and Midwives Award — State 2015, which designates it as “the agreed methodology for the resolution of disputes regarding workload and safe staffing levels.” The Hospital and Health Boards Act 2011 provides the overarching legislative framework for nursing workload management in Queensland, with the BPF as the primary tool used to comply.

WA adapted the Queensland framework under a Creative Commons Attribution 3.0 Australia license. The attribution is documented. The calculation methodology, including the structural error, was carried forward intact. The error did not enter WA’s framework through a new mistake. It entered through trust: Queensland published it, Queensland used it for decades, and WA adopted it without reviewing the underlying formula.

This is how institutional errors travel. Not through negligence. Through the reasonable assumption that a methodology used for 24 years under industrial and statutory authority has been verified.

It has not.

The Timeline

In a previous article, I documented this error appearing in a formally published, institutionally endorsed American document from 1960. The most recent confirmed instance is the WA framework published in February 2026. Between those dates: textbooks, continuing education programs, consulting materials, professional organization publications, and personal communications from nurse managers inside the U.S. Department of Veterans Affairs. More than 30 documented sources (and growing) spanning the United States, the United Kingdom, Saudi Arabia, New Zealand, and now two Australian jurisdictions. Academic peer review, professional credentialing bodies, and government workforce planning processes have all carried this error forward without catching it.

It does not respect credential, institution, or geography.

The Compounding Problem

Australia’s own government Nursing Supply and Demand Study projects a shortage of 70,707 nurses by 2035. The Australian College of Nursing is requesting $891 million over four years to address it through workforce pipeline investment, scope of practice reform, and expanded care models. That investment addresses the supply side of the equation.

The calculation error in this article operates independently of the supply problem. It creates a structural FTE deficit that exists regardless of how many nurses Australia trains, recruits, or retains. Closing the 70,707 gap does not correct a budget formula that produces 5.5 fewer FTEs per 100 than adequate staffing requires. Both problems are real. Neither cancels the other.

What This Means for Ratio Legislation

Mandating nurse-to-patient ratios is a legitimate policy response to unsafe staffing. The research supports the goal. The legislative mechanism takes the problem seriously in a way that voluntary guidelines cannot.

But legislation mandates outcomes. Mathematics determines whether those outcomes are achievable. Western Australia mandated the ratios. It did not verify whether the formula to fund them is correct.

I have written previously about why ratios are an incomplete solution to nurse understaffing — they address the patient care dimension while leaving the time-off and education budget requirements unresolved. This article documents a more foundational problem: the formula used to calculate the workforce required to meet the mandated ratios is structurally incorrect. A facility committed to full compliance with the law cannot guarantee ratio adherence if its budget calculation is producing a short answer.

You can satisfy the legal requirement on paper, using the government’s own methodology, and still be systematically understaffed. Understaffing at that level, relative to what adequate staffing requires, is exactly the condition that JAMA Network Open research found associated with 22% higher odds of in-hospital death.

That is not a hypothetical. It is an arithmetic outcome with documented clinical consequences.

What This Asks

This is not an argument against ratio legislation. It is an argument for verifying the math before the law takes effect, and for reviewing that math when adapting another jurisdiction’s framework, even one with a 24-year statutory track record.

For nursing leaders in Australia and anywhere else ratio legislation is being developed or implemented: the ratio specifies how many nurses a patient deserves. The budget formula determines whether the facility can afford to put that number on the floor. Both questions require a correct answer. Right now, one of them does not have one.

The correct formula:

Total FTEs = Patient Care FTEs ÷ Patient Care %

Patient Care Support FTEs = Total FTEs − Patient Care FTEs

At WA’s own leave entitlement rates, the correct answer is 100 total FTEs for every 76.54 Patient Care FTEs. Not 94.50.

The formula is one operation. It is not expensive to correct. It does not require legislation, a workforce strategy, or an $891 million investment. It requires a decision to check whether the math underneath the mandate is right.

Australia passed a law because understaffing costs lives. The formula used to implement that law ensures the staffing required to save them is never fully funded. That is not a policy failure. It is an arithmetic error. And arithmetic errors have solutions.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and conceptual approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

Tess Michaels gave this talk at TEDxNightingale College’s inaugural event in Salt Lake City on February 24, 2026, a gathering that brought national healthcare, education, and workforce leaders together around a theme of system transformation. Michaels is founder and CEO of Clasp, a company building what she describes as a private-market ROTC framework: hospitals identify promising nursing students, commit to loan repayment tied to multi-year employment, and move the recruitment relationship upstream from the job board to the classroom.

“The math stopped working” is the right diagnosis. The question is when it broke. She locates the break at the point nurses start comparing offers.

The math broke earlier.

A widely used nursing budget formula contains an arithmetic error that produces a systematic FTE shortfall before the fiscal year begins. Hospitals using the common methodology hire fewer nurses than the workload actually requires. That shortfall runs through the schedule as overtime dependency, which accelerates the burnout and exit cycle the sign-on bonus is meant to reverse. The bonus is not a cause. It is a reaction to a demand-side miscalculation baked in at the budget layer.

The formula is not specific to one hospital or one system. It is taught in nursing finance education, embedded in textbooks, carried into nurse manager workshops, and used across facilities of every size. A nurse who takes the sign-on bonus and moves down the road is, in most cases, moving from one facility running the wrong calculation to another running the same one. The understaffing follows. The cycle repeats.

Michaels’ loan repayment model addresses the supply pipeline and a genuine independent variable: student debt as a driver of early-career mobility. It does not address whether hospitals have correctly calculated how many nurses they need in the first place. A nurse committed through a multi-year agreement enters an environment that has not changed. The conditions that produced the turnover are upstream of the retention intervention.

The workforce planning methodology itself is the missing variable. If the formula hospitals use to calculate nursing FTE requirements is structurally wrong, every downstream intervention, whether sign-on bonuses, loan repayment, resilience training, or scheduling technology, tries to manage a problem it did not create and cannot resolve.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and conceptual approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

These are not just different units. They are different ways of understanding what nursing is and what it costs. That difference has structural consequences for how nursing budgets are built, how staffing is evaluated, and why the two disciplines rarely reach agreement in the same room.

How nursing measures itself

The natural unit of nursing measurement is the nursing hour. Shifts are aggregations of hours. Nurse-to-patient ratios are expressions of hours: a 1:4 ratio on a 12-hour shift is 3.0 nursing hours per patient day (NHPPD). A 1:5 ratio is 2.4 NHPPD. The ratio and the NHPPD are the same number stated differently, and nursing moves fluidly between them because both describe something concrete: how much nursing time a patient receives.

Patient Care Support time follows the same logic. Orientation hours, education hours, time off, and administrative time are all measured in hours. The nursing budget, viewed from the inside, is a map of how nursing hours should be allocated across patients, their care needs, and everything that supports that care delivery. None of this translates directly into the language that finance uses to fund it.

How finance measures nursing

Finance starts with the midnight census and a room rate. A percentage of that room rate is allocated to nursing. The result is a bucket of money. That bucket funds the nursing budget.

Room rates can vary by unit, for example, med-surg vs ICU, but do not flex with individual patient acuity within a unit. A relatively stable patient and a highly unstable patient on the same unit generate the same dollar contribution to nursing’s budget allocation, regardless of how much nursing time each actually requires. The hospital cannot charge a different rate based on individual patient complexity. Nursing receives a flat percentage of a fixed rate. Finance is not ignoring this. The reimbursement model simply has no mechanism to encode acuity at the individual patient level.

That misalignment has been growing. A 2024 cohort study in JAMA Internal Medicine examined 3.4 million hospitalizations across 101 hospitals over 15 years and found that hospital inpatients became significantly more complex by nearly every measure. The likelihood of admission via the emergency department nearly tripled. Patients arrived sicker, on more medications, and managing more simultaneous medical problems than at the start of the study period. The patients that hospitals are caring for today require more nursing time per patient than the patients the room rate was built around. The dollar allocation has not moved to reflect that.

What the gap costs

When the unit of measure differs, the translation between them becomes a negotiation rather than a calculation. If you are a nurse leader, you have been in that negotiation. You present hours and ratios. Finance presents variance reports and FTEs. Neither of you is wrong on your own terms. Both of you are describing the same workforce through incompatible lenses.

Three structural consequences follow from this.

First, the reimbursement model places a ceiling on nursing labor dollars that does not move with patient complexity. This is not a budget decision. It is a design feature of how hospitals are paid.

Second, the budget formula used to calculate nursing workforce requirements contains specific and correctable errors, including one that produces a short answer by multiplying when it should divide. Applied to nursing budget calculations, it consistently produces a lower FTE count than adequate staffing requires, reducing effective nursing labor budgets by 2 to 10 percent at many organizations for decades. It has been reproduced across textbooks, credentialing programs, and government frameworks without recognition. I have documented it in prior articles. It compounds the reimbursement ceiling rather than correcting for it.

Third, Patient Care Support time was historically treated as a staffing buffer. When census ran high, education and orientation hours were deferred, and those nurses were redirected to the bedside. This worked as long as vacancy rates were low enough for the buffer to exist. Elevated vacancy rates eliminated the buffer.

The consequences of depleting that buffer compound over time. Understaffing drives burnout. Burnout drives turnover. Turnover replaces experienced nurses with early-career nurses who require more orientation and education to reach the same level of competency. A 2026 government-commissioned study of Minnesota’s hospital nursing workforce, consistent with national trends, found that nearly 40 percent of hospital bedside RNs are under 35, and one in five has fewer than 2 years of nursing experience. The share of nurses 55 and older dropped from 20 percent of the hospital bedside workforce in 2019 to under 14 percent by 2024. One nurse in a focus group described the experience directly: “Patients back when I started were not as sick as they are today...they’ve taken away staff to take care of these sicker and sicker patients.”

The staffing reductions she describes are not always the product of intent. A budget formula that has been producing a short answer for decades creates the same experience at the bedside as a deliberate cut: a younger, less experienced workforce caring for a more complex patient population, with less Patient Care Support time available for education, time off, and adequate orientation. The result is more overtime, less recovery time, and a burnout cycle the budget models were never designed to see, let alone correct.

Rising patient complexity compounded the problem from the other direction. More complex patients require nurses with greater knowledge of advanced comorbidities, technologies, drugs, and treatments. Maintaining that competency requires ongoing education. The demand for Patient Care Support time was increasing at the same time the supply of it was being consumed by vacancy-driven staffing gaps. The budget models that depended on the buffer have not been updated. The education investment those models never fully funded is now more consequential for patient safety than it has ever been.

Three separate structural problems, each compounding the others, all traceable to the same root: two disciplines describing the same workforce in units that do not translate.

The bridge

The measurement gap is not inevitable. It persists because the two disciplines have not had a shared language or a common calculation framework.

Patient-centric budget terminology replaces “productive” and “nonproductive” FTEs with Patient Care FTEs and Patient Care Support FTEs. The new terms describe what the time does rather than assigning an industrial productivity judgment to nursing activities.

Care-centric modeling provides the calculation framework. Starting from available FTEs, the model deducts required Patient Care FTEs first, protecting the bedside. It then accounts for orientation and leave, yielding the Available Patient Care Support FTEs: the time that can actually be used for education, time off, and orientation without creating open shifts. The output is expressible in FTEs for finance or in hours and shifts for nursing. A nurse manager who understands care-centric modeling knows that a budget cut of 2.1 FTEs is not an abstraction. It is 84 hours, or seven 12-hour shifts, removed from the schedule every week. The same calculation that produces a number finance can act on produces a staffing reality nursing can see. That is the Rosetta Stone: not a compromise between two languages, but a single framework that speaks both.

Fully resolving the structural ceiling described in this article requires reimbursement reform that accounts for individual patient acuity, rising complexity, and the true cost of maintaining a competent nursing workforce. Until that reform arrives, patient-centric budget language and care-centric modeling give organizations an actionable path forward: tools that make the gap visible and measurable, and a shared framework for nurse leaders and finance to understand what adequate staffing actually requires.

The patient-centric language and care-centric modeling tools are available. The harder question is whether organizations are ready to use them. What would it take for yours to start?

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and logical approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

In 1960, a woman named Sister Mary Laura Gunn sat down to solve the nursing shortage.

She had credentials: a Bachelor of Science in Nursing, a Master of Hospital Administration (M.H.A.) from Saint Louis University, and a graduate program director, Charles Berry, Director of the Department of Hospital Administration at Saint Louis University, who believed her work was important enough to endorse in print. Her monograph, A Method for Developing a Master Staffing Plan for the Nursing Service Department, was published by the Catholic Hospital Association. It was based on real data from an actual hospital. It was serious and credentialed work.

Her diagnosis was exactly right.

“The ever-present excuse — a shortage of such personnel — has resulted in investigations that disprove this in many instances. The real problem lies in faulty planning by the administrators of the nursing service department, resulting in the ineffective utilization of the available qualified personnel.”

Berry’s foreword reinforced it:

“Despite the recognized shortages of competent nursing personnel, many authorities have questioned the acuteness of these highly publicized shortages. The lack of application of proven management techniques has been noted by numerous commentators.”

The nursing shortage, she argued in 1960, was not primarily a supply problem. It was a methodology problem. Faulty planning. The wrong tools applied to a real challenge.

She was right. Then she demonstrated the same class of error in her own proposed solution.

The Timeline Just Moved

In my previous article, The Middle-School Math Error Behind the Nursing Shortage and Staffing Crisis, I documented the core calculation error embedded in nursing workforce budgeting: the formula that multiplies instead of divides, producing a structural staffing shortfall before the fiscal year begins. I traced that error to a 1979 source as the earliest documented instance.

I have since identified an earlier one.

Gunn’s 1960 monograph predates that source by 19 years. It is formally published, institutionally endorsed, and written by a graduate-trained administrator who was explicitly applying management methodology to nursing workforce planning. This was not a casual or underqualified source.

The error was not introduced through negligence in a later decade. It appears to be a foundational feature of how nursing staffing methodology was formalized from its earliest documented stage thus far.

What Ward Told Us in 2016, and What Was Already True in 1960

William J. Ward, Jr., MBA, is Associate Professor of Health Finance and Management at the Johns Hopkins Bloomberg School of Public Health and former Director of its Master of Health Administration Degree Program. He served as Chief Operating Officer of Johns Hopkins Bayview Medical Center for over a decade and also lectures at the Johns Hopkins School of Nursing and the University of Maryland School of Nursing. His textbook, Health Care Budgeting and Financial Management, is widely used in healthcare administration programs.

In 2016, Ward wrote this:

“Even those skilled in budget calculations often make the mistake of saying, ‘Let’s add 15% for nonproductive time.’ But this approach is clearly incorrect and will always result in a budget that lacks sufficient staff.”

His worked example: starting from 17 Patient Care FTEs, the multiply method produces 19.55 FTEs. The correct divide method produces 20.00. A shortfall of 0.45 FTEs, nearly 2%, is built into the budget before the fiscal year begins.

Ward wrote that warning in 2016. The formula he described had been in print since at least 1960.

Ward identifies that the method is wrong. What requires closer examination is why it always produces a shortfall, and the structural reason becomes clear when you work through the math.

The Proof: What the Math Is Actually Failing to Calculate

Working through this in hours makes the gap visible in a way that FTE counts alone do not.

Assumptions: 100 total FTEs, 20% Patient Care Support rate, 2,080 hours per FTE per year. Total budgeted hours: 208,000.

Table A: The Correct Method (Divide)

Table B: The Incorrect Method (Multiply)

Table C: What Was Never Calculated

The gap has a structural source.

The multiply method takes the 80 Patient Care FTEs and calculates how much Patient Care Support time they need: 80 × 20% = 16 Patient Care Support FTEs. It stops there.

But those 16 Patient Care Support FTEs also need Patient Care Support time. They take vacation. They call in sick. They attend orientation. They complete required continuing education. They are absent from direct patient care work for the same reasons every other FTE on the unit is absent. The multiply method never calculates their support time. It treats them as if the Patient Care Support percentage does not apply to them, as if they have no absences, no PTO, no education requirements.

The correct method divides Patient Care FTEs by the Patient Care percentage and solves for the unknown total, just like the middle school ratio problem I discussed in my last article. It accounts for the entire workforce, including the Patient Care Support FTEs themselves. The incorrect method accounts only for the Patient Care FTEs and stops one step short.

The result: 160 hours per week, in a 100-FTE workforce budgeted at 20% Patient Care Support, that were never planned for and never funded. That is the source of the structural overtime. That is why the schedule ships next to an overtime sign-up sheet. The budget was wrong before the period started.

66 Years

1960: Sister Mary Laura Gunn publishes a formally endorsed nursing staffing methodology. In the same document, she correctly diagnoses the nursing shortage as a product of faulty planning, and her formula contains the same class of error she diagnosed.

The following decades: The error moves through nursing finance textbooks, conference presentations, consulting engagements, continuing education programs, and leadership workshops. It crosses from the United States into international nursing literature and becomes standard practice.

2016: A Johns Hopkins faculty member with decades of healthcare administration experience publishes a textbook explicitly naming the error, proving it mathematically, and warning readers not to make it. The error remains in common use.

Last week: I sat in a $650 nurse leader workshop where the incorrect formula was taught as standard methodology.

The error is not a historical artifact.

Why has it persisted?

Not because nursing leaders are indifferent to accuracy. The persistence is institutional and has taken specific forms.

Organizational and structural inaction:

• Curriculum inaction. The error was never audited out of training materials, nursing finance curricula, or continuing education programs. What was taught in 1965 was taught in 1985, in 2005, and last week.

• Market inaction. Professional development products built on the incorrect methodology were sold, purchased, and delivered to nursing leaders for decades by consulting firms, professional associations, and workshop facilitators. No correction mechanism existed in the market.

• Peer review inaction. Published literature carrying the error passed through academic peer review without being flagged or corrected. The quality control mechanism the profession relies on to validate knowledge did not catch it.

Individual and inherited inaction:

• Inheritance. Leaders received the methodology as standard practice from their training programs, their textbooks, and the mentors who taught them. There was no signal that anything was wrong. The formula looked reasonable. It produced a number. The number went into the budget.

• Workaround behavior. The shortfall produced by the formula was real and felt. Budgets that never quite worked. Schedules that required overtime as a structural baseline rather than an exception. Targets that couldn’t be hit regardless of how hard the team worked. Most leaders sensed something was off, but the error gave them no way to identify the source. So they managed the symptoms: overtime, agency staff, vacancy management, appeals for more resources, coaching and workshops for burnout and resilience. The response to a calculation error became a permanent operational posture. That is not negligence. That is working within a broken system with the tools you were handed.

But while the institution failed to transmit an accurate methodology, the individual has been held accountable for the results. Nurse managers evaluated on labor budget variances they cannot control. Directors whose overtime numbers are reviewed in monthly operations meetings. CNOs held responsible for staffing outcomes that are structurally impossible to achieve given the formula embedded in their budgets. This is not a performance problem. It is a setup, and it has been a setup for at least 66 years.

What This Asks of You

This article is not an indictment of nursing leaders. It is, if anything, a partial explanation for why so many of you are exhausted.

You received this methodology without any indication that it was broken, from sources you had every reason to trust: your graduate program, your textbooks, your mentors, your professional development. The formula looked like standard practice because it was standard practice. That it was wrong was never part of what you were taught.

▶️ That changes now.

Gunn asked the right question in 1960, even if she didn’t resolve it: Is this a shortage of nurses, or a shortage of accurate math?

The nursing shortage has resisted every solution offered to it for at least 66 years. Supply-side responses, workforce initiatives, recruitment campaigns, retention programs. Some of the problem is workforce. Some of it is math. You now have the tools to separate the two, and the responsibility to do so.

You inherited this methodology. You are positioned to be the generation that corrects it.

▶️ Start here:

Audit your budget methodology. If your current process multiplies Patient Care FTEs by the Patient Care Support percentage to calculate Patient Care Support FTEs, you are using the wrong formula.

The correct formula is division:

Total FTEs = Patient Care FTEs ÷ Patient Care %

Patient Care Support FTEs = Total FTEs − Patient Care FTEs

Run both calculations against your current budget. If the numbers are different, and they very likely will be, the gap you find is not a staffing problem. It is a math problem. And math problems have solutions.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and logical approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

This is the detailed follow-up of the budget error mentioned in my Beyond Productivity: Building a Resilient Nursing Workforce article.

A middle-school math error in commonly used nursing budgeting processes has been a hidden driver of the nursing shortage and hospital understaffing for over 40 years.

This error affects all direct care staff - nurses, nursing assistants, techs, and other support personnel.

And we’re still teaching it.

In my 27 years of experience in nurse staffing and healthcare finance, I’ve discovered this error everywhere.

The error is so insidious that it creates a hidden 2-6% staffing shortage before the fiscal year even begins. Yet when I show healthcare leaders the mathematical proof, the most common responses are:

“We’ve always done it this way.”

“Fixing this will cost too much money.”

“That’s interesting...” followed by silence.

Here’s the problem:

When budgeting for nursing staff, hospitals calculate two components: Patient Care Time (patient care hours) and Patient Care Support Time (coverage for PTO, education, sick leave, and administrative time - sometimes called nonproductive or replacement time). (Read my article on patient-centric terms for budget language to learn why I use these terms instead of “productive” and “non-productive”.)

For our example unit, we’ll use an 85% Patient Care / 15% Patient Care Support Time split.

Here’s where the math goes wrong:

If each employee spends an average of 15% of their time in patient care support activities, then patient care support time should be 15% of the TOTAL budget - not 15% of patient care hours.

Let me show you the difference:

An FTE, or Full-Time Equivalent, represents the hours worked by one full-time employee - typically 40 hours per week or 2,080 hours per year.

Calculating patient care FTEs is straightforward: A 1:4 nurse-to-patient ratio for 24 patients means 6 nurses per 12-hour shift, resulting in 1,008 hours of patient care per week. Dividing those hours by 40 hours per FTE gives us 25.2 FTEs representing 85% of our total budgeted resources.

Once patient care FTEs are calculated, we need to determine how many additional FTEs are needed for patient care support to cover time off, education, and administrative time.

THE POPULAR WAY (how many hospitals and “experts” incorrectly calculate patient care support):

Unit needs 25.2 FTEs for patient care

Multiply 25.2 x 0.15 = 3.78 patient care support FTEs

Total staffing: 28.98 FTEs

But you actually need 29.65 total FTEs (shown in the image above and below in the correct calculation)

Check the math: 3.78 ÷ 29.65 = only 12.75% patient care support time when you need 15%

In nursing school, we’re grilled on medication calculations: Check your math. Double-check. Triple-check. One error can cause serious harm or death.

Yet budget “experts” calculate patient care FTEs, multiply by 15% for patient care support time, get only 12.75% - and haven’t checked their math in over 40 years.

What applies to one FTE applies exactly to 100 FTEs. This 2-6% error scales across entire systems.

We demand precision in medication calculations because errors cause harm.

This budget error also causes harm - moral injury, burnout, the need for therapy and medication, and, in the most tragic cases, nurse suicide from chronic understaffing.

🚩Red flag: If your budget multiplies patient care FTEs by a replacement percentage or factor, you’re using the wrong formula.

THE CORRECT WAY:

Divide the patient care FTEs by the Patient Care Time percentage: 25.2 ÷ 0.85 = 29.65 total FTEs

This is like solving those middle school ratio problems: 25.2 is to 85 as X is to 100, solve for X

Patient Care Support FTEs: 29.65 - 25.2 = 4.45 FTEs

Check: 4.45 ÷ 29.65 = 15% patient care support time

The shortfall from the flawed formula: 0.67 FTEs per unit.

That’s almost a 23-hour weekly gap that forces nurse managers to either cut scheduled PTO and education time, pay for overtime/agency nurses, or leave shifts unfilled.

The shortfall increases as the budgeted percentage of patient care support increases. At 20% patient care support time, the shortfall jumps to 4%.

For a cleaner example that shows the pattern more clearly, consider a unit that needs 100 total FTEs with 20% budgeted for patient care support.

THE WRONG WAY:

Calculate 80 patient care FTEs (80% of 100)

Multiply 80 x 0.20 = 16 patient care support FTEs

Total: 96 FTEs

Check: 16 ÷ 100 = only 16% when you need 20%

Notice the pattern: the percentage shortfall equals the number of FTEs lost per 100 total FTEs.

The shortfall: 4 FTEs - exactly 4%.

4 FTEs x 40 Hours/FTE = 160 hours / 12 = 13.3 12-hour shifts lost per week.

The proportions of patient care and patient care support (PTO, education, leave, admin time) remain constant when FTEs are aggregated. The higher the budgeted patient care support percentage, the worse the shortfall becomes.

The impact is devastating.

Most nursing leaders don’t know their budgets are flawed. It seems they’re being set up to fail from day one, which likely explains the high turnover in nurse manager positions and why schedules get posted alongside overtime signup sheets. Take the 0.67 FTE shortfall from our first example and multiply it across a typical 10-unit hospital: nearly 227 hours or 19 twelve-hour shifts short EVERY WEEK - built into the budget from day one, before factoring in any actual time off, sick calls, education time, or vacancies.

➡️This is how the invisible buffer that actually holds the system together disappears.⬅️

Scale that to a healthcare market or region, and the numbers become staggering.

Bottom line: For every 100 FTEs of nursing staff budgeted using this flawed formula, you lose 2-6 FTEs (see table below).

These gaps get filled by overtime, agency nurses, or go unfilled entirely.

And it gets worse: vacancy rates compound the shortage.

If a unit budgeted for 15% patient care support time but has a 10% vacancy rate, those vacant positions reduce the total available hours. When nurse managers schedule 15% of their reduced staff for PTO and education, patient care shifts go unfilled. Leaving these shifts unfilled creates a cascading staffing crisis. Filling the shifts with premium labor threatens the financial bottom line.

I’ve found this error everywhere:

Journal articles. Textbooks. Conference presentations. Leadership workshops. Consulting materials.

Our professional organizations and independent nursing “experts” have been teaching this flawed formula to nursing leaders for decades.

This flaw has been prevalent in our literature and practice for over 40 years.

I’ve identified the same error in documents from the US, the Philippines, Australia, the UK, Europe, Saudi Arabia, and New Zealand.

We’re debating whether there’s a nursing shortage while the foundational math that determines the size of the workforce we need has been wrong the entire time.

This error is a hidden factor in the global nursing shortage. The longevity and prevalence of this flawed formula strongly suggest we’ve been systematically underestimating our global nursing workforce needs for decades.

What do decades of systematically underestimating nursing resource needs do to staffing, burnout, patient care, and the health of communities served by facilities using these methodologies?

Bad formulas generate bad data. Bad data drives bad decisions. Bad decisions can cause harm. And the error compounds at every level - from unit budgets to hospital staffing to regional workforce planning.

Hospitals are 2-6% understaffed from this error alone. Other budget methodology flaws I’ve identified push the shortage above 10%. Layer on a 10% vacancy rate, and many units are starting with 15-20% fewer resources than needed.

This contributes to nurse burnout, inflated overtime costs, reliance on expensive agency nurses, and poor outcomes.

The math is simple.

Fixing it is complicated. Not because of the calculation, but because of how healthcare finance works. When nursing is viewed as an expense to minimize rather than the essential service that generates revenue, correcting a 2-6% staffing shortage becomes a budget battle.

I’ll discuss the impact of our nursing reimbursement model in more detail in a future post.

We can’t solve what we can’t accurately measure.

It’s time to get the math right.

If you’re a nursing leader: Pull your budget calculations. Check if you’re multiplying by the replacement percentage. If so, you’ve just discovered why your schedules never work.

If you’re a bedside nurse: Ask your manager how patient care support FTEs are calculated. Share this article. Start the conversation.

If you’re in finance: Run the numbers. Divide, don’t multiply. See what you’ve been missing.

This isn’t just about fixing a formula. It’s about building a movement for:

✅ Awareness: Exposing the hidden errors that have understaffed hospitals for decades

✅Accountability: Demanding that professional organizations and experts stop teaching flawed methodologies

✅Real solutions: Using accurate data to address the actual nursing shortage and staffing crisis

I’m working with nursing leaders, informatics professionals, and healthcare innovators to identify these systemic budget flaws and develop care-centric, data-driven, evidence-based solutions to correct them. If you’re ready to be part of the solution, let’s connect.

Question for discussion: Does your facility use this formula? What did you discover when you checked?

My next post: Help me prove to yourself and to our community just how widespread these errors really are. There will be prizes for those who participate. Stay tuned.

The shortage starts with bad math. The movement to correct it starts with you.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and logical approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

We’re measuring ourselves into system failure while calling it productivity and performance optimization.

On the dashboards, system collapse rarely announces itself. Nursing operations stay focused on productivity and patient care metrics that look manageable - right up until the system breaks.

In complex systems, failure happens when the margin between load and capacity disappears. The buffer that absorbs variability gets consumed in the name of efficiency.

This is exactly what’s happening in nursing workforce planning and management.

Most nursing reports and dashboards track performance metrics: nurse-to-patient ratios, direct care hours, and productivity percentages. They measure utilization and output without considering what it takes to maintain that performance.

But they miss the disappearing buffer.

In nursing systems, that buffer is the time for patient care support. Time off that actually gets used. Continuing education hours. Administrative time for care coordination. Documentation without rushing. Transition time between complex cases.

We’ve labeled these activities “nonproductive time” and systematically minimized them to drive up bedside time percentages.

The financial structure of healthcare reinforces this thinking. Unlike every other profession in the hospital, nursing care is bundled into the room rate - much like housekeeping in a hotel charge. Nurses cannot direct bill for their time, which means hospitals can’t charge more for patients who require more intensive nursing care. This invisibility in the revenue model positions nursing in finance’s view as a cost center to be cut, managed, and constrained rather than a revenue generator to be invested in. When patient care resources are stretched, the buffer becomes the release valve - education hours, administrative support, and adequate relief get reallocated to cover direct patient care.

Our budget methodologies reinforce this thinking. We calculate FTEs based on productive hours per year, assuming nurses will work at or near full capacity. We build staffing models based on target patient ratios without accounting for the time required to sustain them. Annual budgets treat buffer capacity as waste to be cut or eliminated rather than infrastructure to be maintained. Inadequate planning and long-standing, flawed budget calculations chip away at the buffer before a manager even makes their first schedule.

Each productivity optimization chips away at resilience. Consider this scenario: A nurse skips lunch to cover an unplanned admission. That same week, her scheduled education day gets pulled to cover call-outs. The following month, her PTO request for her daughter’s graduation gets denied because the unit is short-staffed. By quarter’s end, she’s working every weekend to help the team. The metrics look fine - patient ratios are maintained, productivity is high. But her capacity to absorb the next surge, the next crisis, the next ask? It’s gone.

The system looks efficient right up until it collapses - mass resignations, burnout, safety events, beds sitting empty because there’s no one to staff them. Leaders wonder how it happened.

It wasn’t sudden. We’ve been watching the collapse happen in slow motion. But we’re so focused on patient care productivity that we don’t know how to stop it.

The buffer eroded gradually, shift by shift, until there was nothing left to absorb the next crisis.

So what does sustainable workforce planning actually look like? It starts with protecting the buffer, not eliminating it.

Instead of just tracking direct care hours, we need metrics that measure system resilience: actual PTO utilization rates, time between high-acuity assignments, educational hours completed per nurse, and availability of administrative support.

This requires leaders to interrogate their budgets and ensure the formulas are correct and that they’re right-sized for actual operations - not just direct patient care, but also the patient care support resources that make sustained performance possible.

Start this week by challenging your budget calculations. Do your FTE formulas account for the true needs for time off, education, and administrative tasks? Or are operations stealing these resources to prop up patient care?

If you need help auditing your workforce budget for hidden buffer erosion, I’m available for consulting engagements. Let’s build budgets that create sustainable systems, not just manageable metrics. Message me to discuss your situation.

The strongest nursing systems aren’t the most efficient ones. They’re the ones with enough margin to bend without breaking.

The question isn’t whether we can afford to protect the buffer. It’s whether we can afford not to.

▶️ You can learn more about the specific budget error referenced above in my follow-up article: The Middle-School Math Error Behind the Nursing Shortage and Staffing Crisis.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and logical approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

Finance cuts “nonproductive” FTEs. Six months later, labor costs are higher than before. This keeps happening. Here’s why.

We’ve been using management engineering terminology from the 1960s to describe nursing activities. It’s outdated, and it’s doing real damage.

When we label education, orientation, and professional development as “nonproductive,” what message are we sending?

When time off for rest and recovery gets the same label as waste and inefficiency?

Our budget language is working against nursing retention and professional growth.

The traditional “productive” versus “nonproductive” FTE model came from industrial management. Made sense for manufacturing widgets. Makes zero sense for patient care.

But here’s the thing: every nursing activity serves patients. Some directly at the bedside. Others through education, competency development, and making sure nurses are rested enough to provide safe care.

The language we use matters. It affects how leaders think about resource allocation, which activities get cut first during budget constraints, and how nurses feel about their non-bedside time.

The solution is straightforward.

Instead of “productive” and “nonproductive” FTEs, I propose Patient Care FTEs and Patient Care Support FTEs. The first covers direct patient care activities. The second covers education, time off, and administrative tasks that make safe, effective, and affordable care delivery possible.

This language shift is strategic. Yes, it’s a terminology change - but language shapes how we prioritize and allocate resources. When patients are central to our budget conversations, different decisions get made. Support activities get the respect they’ve earned. Leaders think twice before cutting. A 2.1 FTE reduction in Patient Care Support? That’s 84 hours. Seven 12-hour shifts. One shift lost every single day of the week, every week.

I recently met with an ICU manager whose finance team slashed his Patient Care Support FTEs (also called replacement FTEs) from 9.86 to just 5.00 - dropping his Patient Care Support percentage from 13.53% to 7.35%. The result? After cutting 4.86 nonproductive FTEs from his budget, he now needs 4.5 FTEs of expensive travel RNs to meet his staffing requirements. Cutting “nonproductive” time didn’t save money. It increased labor costs. In my work, I’ve found that every $1 of unplanned Patient Care Support time can turn into $2-$4 down the road when those resources get backfilled with premium labor. That matters when so many facilities operate on razor-thin margins.

Healthcare organizations gain a way to talk about nursing resources that actually reflects the value and reality of the work. Patient-centered language in financial discussions changes how we think about allocating resources.

Look at our current workforce reality. Nurses are leaving bedside roles at unprecedented rates. Baby boomers are retiring in massive numbers. New graduate tenure is declining. Burnout rates remain dangerously high.

National reports consistently identify high workloads and insufficient support as primary factors in burnout. So when we frame education and development time as “nonproductive,” we’re feeding the problem. And we’re telling bedside nurses that their professional development doesn’t matter. When we call it Patient Care Support instead, we’re signaling that their growth and well-being are valued parts of delivering quality care.

And when budget cuts target “nonproductive” time? We’re actually cutting Patient Care Support resources. The terminology obscures what’s really happening: we’re reducing the very things that keep nurses competent, rested, and able to deliver safe care.

During budget planning, nonproductive time is carefully calculated - estimating the sick time, vacation, education hours, and orientation that employees will need. The disconnect here is that cutting those FTEs from the budget doesn’t mean employees won’t need or take that time. They still get sick. They still need vacation time to prevent burnout. New hires still need orientation. The work still has to get done - just now with premium labor instead of planned resources.

Workforce competency isn’t optional for patient safety - but that’s exactly what we put at risk when we underfund “nonproductive” time.

Patient-centered budget language recognizes that all nursing activities contribute to care delivery.

Every hour spent on orientation makes safer patient care possible. Every continuing education session builds competency that benefits patients. Every rest day helps prevent the errors that come with exhaustion.

Call it what it is: Patient Care Support.

As an informatics nurse, I’ve analyzed workforce data across multiple organizations. Small language changes create big cultural shifts in how we value nursing work.

Through mathematical modeling, I can demonstrate that as available Patient Care Support resources decrease, our capacity to provide safe, effective, and affordable care decreases as well.

Take action:

Start using Patient Care and Patient Care Support in your next budget discussion. Update your departmental terminology. Begin thinking about traditional “nonproductive” time as what it really is: Patient Care Support time. When finance talks about cutting “nonproductive” FTEs, reframe it: “You’re proposing we reduce Patient Care Support resources by X hours per week - that’s Y fewer shifts for education, orientation, and time off.”

Make the impact visible. Make the language patient-centered.

Follow me for more on nursing workforce strategy and evidence-based workforce planning and management.

If you’re a nursing leader who suspects your budget numbers don’t add up - or you’re tired of making impossible math work every month - let’s talk. I help nursing leaders understand the real story their budgets and staffing data are telling them.

What terminology does your organization use for nursing FTEs? Have you seen the fallout from calling education and development time “nonproductive”?

It’s time we changed this conversation.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and logical approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

We don’t know if there’s a nursing shortage because we don’t know how many nurses we need.

One side points to understaffed units and burned-out nurses leaving the bedside. The other side points to hundreds of thousands of licensed nurses not practicing in hospitals.

Both sides are missing the fundamental issue.

The real question that isn’t being asked: Are we calculating the right number of nursing staff we need? In many cases, we’re not.

For over 40 years, the nursing industry has built budgets, staffing plans, and workforce projections based on mathematical and logical errors that systematically underestimate resource needs. These aren’t isolated mistakes at struggling hospitals. They’re methodological flaws embedded in our standard practices.

The same flawed methodologies are being taught by nursing experts and high-profile professional organizations in workshops for nursing leaders across the country. We’re not just perpetuating these errors - we’re institutionalizing them at scale.

I’ve spent over 25 years as a board-certified informatics nurse working in healthcare finance and workforce management. I’ve identified calculation errors so pervasive that one common mistake alone produces a 2-6% shortfall in nursing resources across entire organizations. When you’re already operating at the margins, that shortfall becomes the difference between adequate staffing and constant crisis.

Think about what this means for a 500-bed hospital.

A 2-6% resource shortfall translates to dozens of missing positions - nurses, nursing assistants, techs. Not positions that were eliminated due to budget cuts. Positions that never existed because the math incorrectly said they weren’t needed.

Now scale that across an entire healthcare market. If the majority of facilities in a region are all budgeting with the same error, the cumulative undersupply of healthcare workers artificially tightens the labor market and drives up competition for an already insufficient workforce.

Every time we debate whether there’s a nursing shortage, we’re arguing over symptoms without understanding the root cause. We can’t definitively answer the question because we don’t have a valid baseline.

We’ve been measuring the wrong things and using the wrong formulas for decades.

Throughout my career, I’ve participated in the selection, implementation, management, and support of workforce management systems. I’ve seen the same pattern repeated: sophisticated technology deployed on top of fundamentally flawed staffing calculations.

The technology works perfectly. It just automates the mathematical dysfunction.

Here’s what happens when your foundational calculations are wrong:

Your carefully balanced budget still leaves units scrambling every shift. Managers burn out trying to make impossible math work. Nurses leave because the workload feels unsustainable - because it literally is, based on the flawed resource calculations.

The nursing shortage debate intensifies because nobody can explain why well-funded organizations with state-of-the-art systems still can’t maintain adequate staffing levels.

We’ve been solving for the wrong variable this entire time.

We budget for nurses at the bedside for patient care shifts. But we fail to correctly calculate the nurses needed to provide coverage for time off to rest, recharge, and avoid burnout; education to maintain workforce competency; and administrative tasks needed to maintain the care environment. We’re planning for direct care while ignoring everything that makes sustainable, competent direct care possible.

The scope of this problem is staggering. These flawed calculation methods aren’t confined to a few struggling hospitals or one region. This is a global problem. I’ve identified these same errors in the US, UK, Europe, Saudi Arabia, New Zealand, and the Philippines. They’re being used by major health systems, taught in professional development programs, and embedded in industry-standard workforce management practices worldwide.

When I audit staffing methodologies across different organizations, I see the same fundamental errors repeated with remarkable consistency. The mistakes are so deeply embedded in our practices that they’ve become invisible. We’ve normalized mathematical errors as standard procedure.

Consider the ripple effects:

Healthcare organizations are investing millions in retention programs, sign-on bonuses, and travel nursing contracts to address what they perceive as staffing shortages. But if the underlying resource calculations are wrong, these expensive interventions are treating symptoms while the root cause remains untouched.

Nursing schools are expanding programs and rushing graduates to market to fill what appears to be unprecedented demand. But if we’re systematically underestimating the number of nurses we actually need, we’re still not producing enough graduates to meet the real requirements.

Policy makers are debating immigration reforms and federal funding for nursing education based on workforce projections built on the same flawed calculations that created the apparent shortage in the first place.

We’re making multi-billion dollar decisions based on faulty math.

The debate shouldn’t be “is there a nursing shortage?” It should be “how do we fix the calculation errors that have hidden our true workforce needs for decades?”

We can’t solve what we can’t accurately measure. And right now, we’re measuring wrong.

This isn’t about pointing fingers or assigning blame. It’s about recognizing that we have a data problem masquerading as a workforce problem. We have calculation errors masquerading as staffing shortages.

The nursing profession deserves better than building our entire workforce strategy on faulty math.

Healthcare organizations deserve better than investing millions to fix the wrong problem.

Patients deserve better than staffing decisions based on broken calculations.

The urgency has never been greater. With nursing turnover at record highs and healthcare demands increasing, we cannot afford to perpetuate calculation errors that artificially constrain our understanding of workforce needs.

We need to examine our foundational assumptions. We need to identify and correct the calculation errors that have been hiding in plain sight. We need to use accurate data to understand the true scope of our workforce needs.

Only then can we have a meaningful debate about solutions.

Next week, I’ll break down one specific calculation error that’s been taught as standard practice for decades. I’ll show you exactly how it impacts nursing resources and staffing capacity, using real examples that demonstrate the magnitude of this problem.

The longevity and prevalence of these errors strongly suggest we’ve been globally underestimating nursing needs far longer than anyone realizes.

The data tells a story we haven’t been ready to hear. But it’s time we listened.

What we discover might fundamentally change how we approach the entire nursing workforce conversation.

If you’re a nursing leader who suspects your budget numbers don’t add up - or you’re tired of making impossible math work every month - let’s talk. I help nursing leaders understand the real story their budgets and staffing data are telling them.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and logical approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

We’ve given up on solving the nurse staffing crisis. We’re just managing its symptoms now.

In 2020, AONL, ANA, and HFMA published a joint report titled The Business of Caring: Promoting Optimal Allocation of Nursing Resources. The report called for increased communication and collaboration between nursing and finance to develop outcomes-based nurse staffing models and improve the value of patient care.

In 2022, the Partners for Nurse Staffing Think Tank was launched to identify high-priority areas for recommendations that could be implemented within 12-18 months. The report focused on six priority areas: fostering healthy work environments; promoting diversity, equity, and inclusion; enabling flexible work schedules; addressing work-related stress injuries; implementing innovative care delivery models; and developing customizable total compensation programs.

These recommendations aimed to improve nurse retention, enhance patient outcomes, and create sustainable healthcare systems through actionable strategies for health system leaders, policymakers, and nursing organizations.

That was the plan. So what happened?

Since then? Little to nothing. No progress reports. No implementation updates. No accountability.

It’s now 2026, and based on the continued reports raising the alarm about the number of nurses considering leaving the bedside, along with the rise of many coaches, consultants, and workshops promoting anti-burnout, anti-bullying, resilience, wellness, and how to move “beyond the bedside,” one thing is clear.

The staffing problem has gotten worse.

And we’ve largely given up on identifying and addressing the root causes of our staffing difficulties.

Further highlighting the surrender is the American Nurses Association’s (ANA) recent release of its draft position statement, “Fostering Civility and Respect in Nursing: Empowering Nurses to Prevent Incivility and Bullying.” The draft urges individual nurses to “foster a culture of civility,” “engage respectfully,” and “advocate for themselves” through reporting or education.

Here’s the problem: The report’s recommendations imply that individual nurses bear responsibility for countering behaviors often rooted in systemic pressures.

The draft takes a victim-blaming tone, placing primary responsibility on individual nurses while relegating systemic and structural elements like the reimbursement model, oppressed group behavior dynamics, and flawed workforce planning to a brief mention—despite these being the very factors that foster environments more susceptible to bullying and incivility.

To be clear: anti-burnout programs, wellness initiatives, and resilience training have value. They can help individual nurses cope in the short term. But they are not sustainable, long-term solutions to the systemic drivers of the staffing crisis. They’re comfort measures, not cures.

This pattern is the problem. By focusing on “treating” downstream effects while ignoring the need for “curative” measures for upstream systemic and structural causes, we’ve created a failure-to-rescue scenario in which the nurse staffing problem is placed in palliative care.

There’s a better path forward. Florence Nightingale transformed healthcare not by teaching soldiers to cope with poor conditions, but by using data to expose systemic failures and demand change. We can do the same.

We’ve had the data for years. We know exactly what’s driving nurses away. We’re just choosing to treat the symptoms instead of fixing the true root causes.

Now we need to reclaim nursing’s heritage as data scientists. That means rigorous analysis, care-centric and data-driven evidence-based solutions, and the courage to address root causes rather than symptoms. The staffing crisis can be solved. But only if we’re willing to take it out of palliative care, commit to a cure, and do the hard work needed to develop it.

I know this is controversial. But we need to have this conversation. What do you think? Are we treating symptoms or addressing root causes? Let me know in the comments.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and logical approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).

As nurses, we know the toll that incivility and bullying take on our profession. It undermines patient safety, erodes well-being, and drives workforce instability. That’s why I appreciate the American Nurses Association (ANA) opening its draft position statement, “Fostering Civility and Respect in Nursing: Empowering Nurses to Prevent Incivility and Bullying,” for public comment. Drawing from my clinical experience and 26 years in nurse staffing and scheduling operations, here are my initial thoughts on opportunities to strengthen the statement. Today, I’m sharing it here on LinkedIn to spark discussion among fellow nurses, leaders, and healthcare stakeholders. Let’s talk about how we can address not just the symptoms, but the root causes of these issues.

Introduction

The ANA’s draft is a commendable effort to address incivility (low-intensity, disrespectful behaviors) and bullying (repeated acts of hostility involving power imbalances) in nursing. It emphasizes shared accountability, trauma-informed approaches, and evidence-based interventions. These are steps that could foster healthier work and learning environments. However, the draft risks overemphasizing individual actions while underplaying systemic drivers, potentially limiting its impact on prevention.

Key Concerns

A Victim-Blaming Tone

Grounded in the ANA Code of Ethics for Nurses, the draft urges individual nurses to “foster a culture of civility,” “engage respectfully,” and “advocate for themselves” through reporting or education. Recommendations such as upholding the Code, building relationships, or using code words for bullying imply that nurses bear primary responsibility for countering behaviors often rooted in systemic pressures.

This can come across as victim-blaming, especially when considering oppressed group dynamics: Nurses, historically marginalized in healthcare hierarchies, internalize oppression and express it through horizontal violence. With secondary traumatic stress affecting 64-76% of nurses, many lack the capacity to “lead by example” or “insist on open communication.” Instead, these should be framed as shared failures, with greater emphasis on organizational accountability.

Inadequate Attention to Structural Elements

The draft mentions “systemic stressors” such as hierarchies and workloads, but doesn’t delve deeply into how nursing’s structural flaws create environments prone to incivility. For instance, the reimbursement model that bundles nursing care into room rates, making nurses the only major hospital profession unable to bill directly. This establishes nursing as an expense that needs to be minimized, eroding the business case for staffing and contributing to chronic understaffing. Understaffing fuels stress, scarcity, moral injury, and burnout, which can contribute to breeding incivility.

Further flaws in workforce planning worsen this issue: Multiple long-standing mathematical and logical errors in common nursing workforce budgeting methodologies, such as miscalculating non-productive time (for example, multiplying productive FTEs by 15% instead of dividing by 0.85 for a 15% buffer), can result in 2-10+% resource shortages. Creating budgets based on midnight census ignores the reality that resource decisions made at the shift level may not align with budget assumptions based on an average census or a single census point like the midnight census. For instance, a unit with a 1:4 ratio, a 7 AM day census of 25, and a 7 PM night census of 23 would need 6 nurses per shift to meet the ratio if only considering the midnight census of 23 or the average census of 24. However, operationally, a 7th nurse is necessary to maintain the ratio on the day shift. If this pattern continued throughout the week, the unit would require an additional 2.1 FTEs (7 12-hour shifts) to staff adequately. Coupled with reimbursement invisibility, these barriers hinder hiring for acuity or patient volume surges, and increase the risk of understaffing, creating an environment more prone to lateral violence. Employer recommendations (e.g., culture assessments) tend to be reactive; the draft should promote reforms like direct reimbursement for nursing care and evidence-based budgeting and management methodologies to support sustainable staffing for healthier working environments.

Insufficient Focus on Root Causes

While noting impacts like turnover and care compromises, the draft underplays roots like oppressed group behavior theory, where subordination in hierarchies fosters internalized oppression and aggression, and it also overlooks flawed workforce planning and management practices that heighten the risk of understaffing and vulnerable work environments.

This treats incivility as an isolated phenomenon rather than as symptomatic of inequities and systemic drivers. To borrow a parable: Rescuers pull drowning people from a stream, focusing on immediate aid. Eventually, one asks, “Why not go upstream to find why they’re falling in?” The draft excels at “pulling people out” via education and interventions, but must address upstream drivers eroding psychological safety and fueling burnout cycles that create environments that foster incivility and bullying.

Recommendations for Strengthening the Statement

To make this a transformative document:

• Integrate oppressed group theory and advocate for upstream reforms, like reimbursement changes, evidence-based budgeting, and evidence-based workforce management.

• Balance individual duties with mandates for organizations to audit structural barriers, including ANA-led federal advocacy.

• Expand the trauma section to connect secondary traumatic stress to understaffing, with monitoring metrics.

Final Thoughts

A revised statement could empower facilities and nursing leaders to prevent incivility at its roots, aligning with ANA’s ethical commitments. We only have a couple of days left, but let’s use this public comment period to push for change that includes upstream solutions to build resilient, equitable environments for all.

© 2026 Robert L Wingo - All rights reserved.

Robert Wingo, BSN, RN, NI-BC is the author of the Nurse Staffing Information Structures (NSIS) framework, a mathematical and logical approach to understanding and describing the nurse staffing life cycle from reimbursement through outcomes. He is a board-certified nursing informatics specialist and Nursing Economics Fellow (FINE Fellowship, Commission for Nurse Reimbursement).